This slidedeck provides a brief introduction to the ethics and risks associated with urban big data and smart cities and was presented at the launch of the Institute for Data, Systems and Society at MIT, Sept 2016
The ethics of urban big data and smart citiesrobkitchin
This presentation discusses the ethical, privacy and security implications of urban big data and smart city technologies and forwards a set of potential solutions for minimizing harms.
Digital Transformation:
Business process re-engineering with digital technologies
Technology used to make existing work more efficient, now technology is transforming the work itself
Example: single shared item lookup process in blockchain supply chain
Productivity gains
Capital investment in technology
Data centers
Blockchain as a Service, Deep Learning nets
Skilled work force development
Train 1000 software developers
Hyperledger, Ethereum, Corda
Machine Learning, AI, Deep Learning
Scale efficiencies
Natural resources, regional strength, large companies
Manage global trade supply chain with blockchain/deep learning
10 Steps to create a sucessfull e-commerce strategy - 10 passi per creare un ...Federico Gasparotto
Quali sono i 10 passi per creare un e-commerce di successo? Value proposition, Business planning, Channels integration, organisation transformation, Direct marketing, Commercial strategy, assortment planning, Product datatsheet, User Experience
Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (WS...Krishnaram Kenthapadi
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias / discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a "fairness by design" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness-aware machine learning techniques in practice by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we will present open problems and research directions for the data mining / machine learning community.
Please cite as:
Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kiciman, and Margaret Mitchell. Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. WSDM 2019.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
The ethics of urban big data and smart citiesrobkitchin
This presentation discusses the ethical, privacy and security implications of urban big data and smart city technologies and forwards a set of potential solutions for minimizing harms.
Digital Transformation:
Business process re-engineering with digital technologies
Technology used to make existing work more efficient, now technology is transforming the work itself
Example: single shared item lookup process in blockchain supply chain
Productivity gains
Capital investment in technology
Data centers
Blockchain as a Service, Deep Learning nets
Skilled work force development
Train 1000 software developers
Hyperledger, Ethereum, Corda
Machine Learning, AI, Deep Learning
Scale efficiencies
Natural resources, regional strength, large companies
Manage global trade supply chain with blockchain/deep learning
10 Steps to create a sucessfull e-commerce strategy - 10 passi per creare un ...Federico Gasparotto
Quali sono i 10 passi per creare un e-commerce di successo? Value proposition, Business planning, Channels integration, organisation transformation, Direct marketing, Commercial strategy, assortment planning, Product datatsheet, User Experience
Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (WS...Krishnaram Kenthapadi
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias / discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a "fairness by design" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness-aware machine learning techniques in practice by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we will present open problems and research directions for the data mining / machine learning community.
Please cite as:
Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kiciman, and Margaret Mitchell. Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. WSDM 2019.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
In this webinar we divulge our unique approach to video advertising, audience, and campaign planning. We also unpack how to holistically measure your ad campaigns and attribute video conversions with metrics like reach and frequency.
Modeling The Market Mix Modeling Problem (Media Mix Optimization)Amit Satsangi
Channel Attribution Modeling is not the best way to decide on Media Mix Optimization (Channel ROI). Here I present results by formulating the problem as a Marketing Mix using two models:
(a) Linear Regression Analysis
(b) Log-linear Multiplicative Model
La migración a Google Analytics 4 (GA4) ya es un hecho y su implementación ya no es novedad. Ahora vamos al siguiente paso: Cuáles son las capacidades de análisis que ofrece esta nueva versión y cómo obtener información valiosa para mejorar tu estrategia digital.
Tracxn - Top Business Models - Blockchain Industry Applications - Mar 2022Tracxn
Today's top #BusinessModel report is on Blockchain - Industry Applications rebrand.ly/ivw9x2c
Get our free reports on geography of your interest to your mailbox regularly https://rb.gy/cx2upn
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
Data Ethics in the Workplace: Beyond AI, Privacy and SecurityCase IQ
The rate at which we generate data is increasing at a dizzying pace, with estimates of 463 exabytes of data being created each day by 2025. While security and privacy are often top-of-mind when discussing data, organizations must also consider the ethics of data collection, analysis and movement and the risks that they present. Operating without a data ethics framework can cause permanent damage. On the other hand, a healthy approach to data ethics has immeasurable benefits.
Join Neal O’Farrell, data ethics expert, advisor and trainer, as he tackles this timely and important topic. Learn how to create a culture of data ethics in your workplace that can make almost everything better - from AI and big data, security and privacy, trust and reputation, even workplace harmony and productivity.
How Big Data impacts the Gaming Industry. This presentation talks about exponential revenue increase of some of the Big Names such as "Riot (Title: Leagues of Legends), Valve, Socialblade.
I teach a course The Internet Economy. I teach it in a classroom setting and online through Eliademy.com. These slides are the latest from a classroom course in India New Delhi in November 2017
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
In this 21st century, data holders possess more power than governments. The ability to influence the opinion of people lies with them. Revisiting the Cambridge Analytica data scandal just reassures that nothing is bigger than Big data, not even democracy.
AI in Law Enforcement - Applications and Implications of Machine Vision and M...Daniel Faggella
This presentation was given at an INTERPOL / United Nations events about law enforcement and AI, at INTERPOL's innovation lab in Singapore, July 11th, 2018.
The presentation itself covers nearly a dozen AI-related use cases, along with their possible uses in law enforcement, surveillance, or falsifying evidence.
For more details please follow:
https://medium.com/datadriveninvestor/a-powerful-tool-for-demand-planning-segmentation-a66bfa729360
https://towardsdatascience.com/effective-approaches-for-time-series-anomaly-detection-9485b40077f1
https://aditya-bhattacharya.net/2020/07/20/sales-and-demand-forecast-analysis/
By Aditya Bhattacharya
Automatic fact checking is one of the more involved NLP tasks currently researched: not only does it require sentence understanding, but also an understanding of how claims relate to evidence documents and world knowledge. Moreover, there is still no common understanding in the automatic fact checking community of how the subtasks of fact checking — claim check-worthiness detection, evidence retrieval, veracity prediction — should be framed. This is partly owing to the complexity of the task, despite efforts to formalise the task of fact checking through the development of benchmark datasets.
The first part of the talk will be on automatically generating textual explanations for fact checking, thereby exposing some of the reasoning processes these models follow. The second part of the talk will be on re-examining how claim check-worthiness is defined, and how check-worthy claims can be detected; followed by how to automatically generate claims which are hard to fact-check automatically.
Data-driven urbanism (Amsterdam, Jan 2017)robkitchin
This talk details the shift from data-informed urbanism to data-driven urbanism, the use of urban big data and smart city technologies in urban governance, and outlines various concerns and critiques.
Paper presented at Code Acts in Education, an ESRC seminar at the University of Stirling, January 28th 2014, http://codeactsineducation.wordpress.com/seminars/
In this webinar we divulge our unique approach to video advertising, audience, and campaign planning. We also unpack how to holistically measure your ad campaigns and attribute video conversions with metrics like reach and frequency.
Modeling The Market Mix Modeling Problem (Media Mix Optimization)Amit Satsangi
Channel Attribution Modeling is not the best way to decide on Media Mix Optimization (Channel ROI). Here I present results by formulating the problem as a Marketing Mix using two models:
(a) Linear Regression Analysis
(b) Log-linear Multiplicative Model
La migración a Google Analytics 4 (GA4) ya es un hecho y su implementación ya no es novedad. Ahora vamos al siguiente paso: Cuáles son las capacidades de análisis que ofrece esta nueva versión y cómo obtener información valiosa para mejorar tu estrategia digital.
Tracxn - Top Business Models - Blockchain Industry Applications - Mar 2022Tracxn
Today's top #BusinessModel report is on Blockchain - Industry Applications rebrand.ly/ivw9x2c
Get our free reports on geography of your interest to your mailbox regularly https://rb.gy/cx2upn
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
Data Ethics in the Workplace: Beyond AI, Privacy and SecurityCase IQ
The rate at which we generate data is increasing at a dizzying pace, with estimates of 463 exabytes of data being created each day by 2025. While security and privacy are often top-of-mind when discussing data, organizations must also consider the ethics of data collection, analysis and movement and the risks that they present. Operating without a data ethics framework can cause permanent damage. On the other hand, a healthy approach to data ethics has immeasurable benefits.
Join Neal O’Farrell, data ethics expert, advisor and trainer, as he tackles this timely and important topic. Learn how to create a culture of data ethics in your workplace that can make almost everything better - from AI and big data, security and privacy, trust and reputation, even workplace harmony and productivity.
How Big Data impacts the Gaming Industry. This presentation talks about exponential revenue increase of some of the Big Names such as "Riot (Title: Leagues of Legends), Valve, Socialblade.
I teach a course The Internet Economy. I teach it in a classroom setting and online through Eliademy.com. These slides are the latest from a classroom course in India New Delhi in November 2017
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
In this 21st century, data holders possess more power than governments. The ability to influence the opinion of people lies with them. Revisiting the Cambridge Analytica data scandal just reassures that nothing is bigger than Big data, not even democracy.
AI in Law Enforcement - Applications and Implications of Machine Vision and M...Daniel Faggella
This presentation was given at an INTERPOL / United Nations events about law enforcement and AI, at INTERPOL's innovation lab in Singapore, July 11th, 2018.
The presentation itself covers nearly a dozen AI-related use cases, along with their possible uses in law enforcement, surveillance, or falsifying evidence.
For more details please follow:
https://medium.com/datadriveninvestor/a-powerful-tool-for-demand-planning-segmentation-a66bfa729360
https://towardsdatascience.com/effective-approaches-for-time-series-anomaly-detection-9485b40077f1
https://aditya-bhattacharya.net/2020/07/20/sales-and-demand-forecast-analysis/
By Aditya Bhattacharya
Automatic fact checking is one of the more involved NLP tasks currently researched: not only does it require sentence understanding, but also an understanding of how claims relate to evidence documents and world knowledge. Moreover, there is still no common understanding in the automatic fact checking community of how the subtasks of fact checking — claim check-worthiness detection, evidence retrieval, veracity prediction — should be framed. This is partly owing to the complexity of the task, despite efforts to formalise the task of fact checking through the development of benchmark datasets.
The first part of the talk will be on automatically generating textual explanations for fact checking, thereby exposing some of the reasoning processes these models follow. The second part of the talk will be on re-examining how claim check-worthiness is defined, and how check-worthy claims can be detected; followed by how to automatically generate claims which are hard to fact-check automatically.
Data-driven urbanism (Amsterdam, Jan 2017)robkitchin
This talk details the shift from data-informed urbanism to data-driven urbanism, the use of urban big data and smart city technologies in urban governance, and outlines various concerns and critiques.
Paper presented at Code Acts in Education, an ESRC seminar at the University of Stirling, January 28th 2014, http://codeactsineducation.wordpress.com/seminars/
Big data, new epistemologies and paradigm shiftsrobkitchin
This presentation examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines.
This paper was presented at the 'Towards a Magna Carta for Data' workshop at the RDS in Dublin, Sept 17th. It discusses how considerations of the ethics of big data consist of much more than the issues of privacy and security that it often gets boiled down to, and argues that the various ethical issues related to big data are multidimensional and contested; vary in nature across domains, and which ethical philosophy is adopted matters to the deliberation over data rights.
How Singapore is Paving the Way for Global Smart CitiesSolace
Singapore is at the leading edge of global smart city initiatives and is a good deal of the way along the path to truly transforming the “city nation” for the benefit of its citizens and the redefinition of its government. Everything is on the table for reinvention in Singapore from smart transportation to smart energy to smart buildings to how its citizens receive smarter healthcare.
But what is a “smart city”? A smart city uses technology to deliver services, uses technology to reduce costs for service providers and uses technology to allow citizens to improve the running of the city. With sensors planted measuring everything from traffic to street lights, Singapore has deep insights into the lessons in IoT deployment and delivery on a truly large scale.
On October 13th, 2016 at the Internet of Things Developer meetup, Sumeet Puri presented a social and technical perspective on the Singapore smart cities initiative from two viewpoints: his role as an architect of several Singapore Smart City projects and as a resident of Singapore living through the transformation of his surroundings. Sumeet will share stories about Singapore’s efforts, and give us insights into the technical decisions needed to make this a reality. Sumeet will talk about architecting for IoT at scale, lessons learned from the Singapore Smart City Initiatives and the response of the public to these changes.
Over the past three decades city infrastructure and services have increasingly become digitally networked, programmable and data-driven. Moreover, citizens now regularly use mobile spatial media to mediate their spatial behavior and urban experiences and share information via crowdsourced platforms. As a result we are ever more living in the era of smart urbanism — city systems can be operationally managed dynamically using algorithms processing urban big data, citizens can access and contribute live information about the city, and planners and policy makers can redeploy new streams of data to model and plan the city with increasing granularity. The development of smart urbanism poses opportunities and challenges for urban planning, reshaping how we come to know and govern cities, and this talk will examine these drawing on research conducted in Boston and Dublin.
A short set of slides that accompanied my thoughts as a discussant on papers presented at the alt.conference on Big Data at the Conference of the Association of American Geographers, Tampa, April 8-12, 2014
Platform Urbanism: The politics and practices of data-driven citiesSarah Barns
Presentation delivered as part of an Urban Studies Foundation event showcasing the work of its first cohort of International Post-doctoral Fellows (of which I am one). Presented at the University of Glasgow, April 30 2015.
Rob Kitchin Smart Cities 08th March 2016 (Smart Dublin)Mainard Gallagher
Rob Kitchin is a Professor and ERC Advanced Investigator in the National Institute of Regional and Spatial Analysis at Maynooth University, for which he was director between 2002 and 2013. He is one of Ireland's leading social scientists and was the 2013 recipient of the Royal Irish Academy's Gold Medal for the Social Sciences and received the Association of American Geographers ‘Meridian Book Award’ for the outstanding book in the discipline in 2011.
Urban indicators, city benchmarking, and real time dashboards: Knowing and go...robkitchin
Talk presented at the Conference of the Association of American Geographers, Tampa, April 8-12. First attempt at presenting a paper presently being written for publication.
This slide set examines the contention that opening data is an inherently good thing - that the case for open data is an open and shut case. It sets out a contrary view that whilst open data is desirable, much more critical thinking is required as to what this means in practice and the possible negative implications of opening data, and calls for a wider debate about the relative merits and politics of open data and how we go about opening data.
The Real-Time City? Data-driven, networked urbanism and the production of sm...robkitchin
Keynote talk presented at IGU Urban conference in Dublin, August 9th. The paper discusses the transition from data-informed to data-driven, smart cities and the impact of such a transition on city governance and wider society.
Slides from the Privacy: Insights from Lawyers and Technologiest at Maynooth University, July 1st 2015. The talk argues that privacy is multidimensional in nature; notions and practices of privacy are changing rapidly; has all kinds of direct and indirect effects; technology and industry are running ahead of legislators; there is no teleological inevitability to the emerging privacy landscape; it is incumbent on states to address privacy issues and to find a balance with respect to interests of citizens, states and industry.
SmartCity StreamApp Platform: Real-time Information for Smart Cities and Tran...Cubic Corporation
Stream processing use cases today are often discussed within the world of Big Data, the Internet, social media and log analytics, where data rates in real-time world scenarios rarely exceed 10,000 records per second. The emergence of the Internet of Things and new smart services require real-time responses from data arriving at rates of millions of records per second, with 24x7 operation and support for dynamic updates for business services, data feeds and enterprise integrations. This presentation discusses the requirements for operational real-time systems in an Internet of Things environment, where industrial automation, smart cities and telematics require robust and massively scalable data management platforms.
In China, the five year plan points out that developing smart eco-cities – maximizing the conservation of energy, water, land and materials, reducing pollution, optimizing the transportation, protecting the environment, and improving building comfort, health and safety – is a critical stage for accelerating industrialization, urbanization and rural development providing great opportunities. It is estimated that between 2010 and 2025, around 250 million citizens will move from rural areas into cities. By 2030, 1 billion people out of 1.4 billion in China will live in cities, from 3 to 13 (smart eco-) megalopolises built.
Praxis and politics of urban data: Building the Dublin Dashboardrobkitchin
This paper was presented at the Association of American Geographers meeting in Chicago, April 22nd 2015.
This paper critically reflects on the building of the Dublin Dashboard (www.dublindashboard.ie) from the perspective of critical data studies. The Dashboard is a website that provides citizens, planners, policy makers and companies with an extensive set of data and data visualizations about Dublin City, including real-time information, indicator trends, inter and intra-urban benchmarking, interactive maps, the location of services, and a means to directly report issues to city authorities. The data used in the Dashboard is open and available for others to build their own apps. One member of the development team was an ethnographer who attended meetings, observed and discussed with key actors the creation of the Dashboard and its attendant praxis and politics up to the point of its launch in September 2014. This paper draws on that material to consider the contextual, contingent, iterative and relational unfolding of the Dashboard and the emergent politics of data and design. In so doing, it reveals the contested and negotiated politics of smart city initiatives.
Talk presented at TILT, Tilburg University, Netherlands, 14th March 2019. Relates to the book: Cardullo, P., di Feliciantonio, C. and Kitchin, R. (eds) (2019, June, in press) The Right to the Smart City. Emerald.
Cities around the world are pursuing a smart cities agenda in which digital technologies are used to manage cities. In general, these initiatives are promoted and rolled-out by governments and corporations and enact various forms of top-down, technocratic governance and reproduce neoliberal governmentality. Despite calls for the smart city agenda to be more citizen-centric and bottom-up in nature, how this translates into policy and initiatives is still weakly articulated and practiced. Indeed, there is little meaningful engagement by key stakeholders with respect to rights, citizenship, social justice, commoning, civic participation, co-creation, ethics, and how the smart city might be productively reimagined and remade. This talk advocates for the Right to the Smart City and considers how to produce a genuinely humanizing smart urbanism, both with respect to setting out a normative vision for smart cities rooted in ideas of fairness, equity, care, democracy and the public good, and enacting this vision through citizen-centric tactics.
Your mobile knows a lot about you and that brings a number of business risks – security breaches from company data held in emails or business apps, for example. We highlight the data and security risks of the phone in your pocket. -
See more at: http://www.grant-thornton.co.uk/en/Thinking/Beware-the-secrets-held-in-your-smartphone-/?previouspage=7260
Mobile Devices: Systemisation of Knowledge about Privacy Invasion Tactics and...CREST
This presentation reviews privacy concerns for mobile devices and outlines the importance of privacy engineering in ensuring users have safe access to their devices.
Big data and smart cities: Key data issuesrobkitchin
This presentation was delivered at the first meeting of the Irish Government Data Forum, July 14th 2015. It was designed to provide an overview of key data issues related to smart cities in order to set the scene for a discussion about the kinds of data issues the forum might explore across a range of domains.
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Webcast title : GDPR: Protecting Your Data
Description : Find out why data protection and encryption is an essential component of preparing for your GDPR readiness process.
Specifically, we will cover:
What is considered "Personal Data" and why it needs to be "protected"
The Legal Aspects of Data Protection under GDPR.
The technical ways to protect/pseudonymization
In this Session you will learn from the leading experts:
- Ulf Mattsson: The father of database Encryption.
- Martyn Hope: The Co-Founder of the GDPR Institut.
- Mark Rasch: Former Chief Cybersecurity Evangelist at Verizon and led the DOJ's Cyber Crime Unit.
Presenter : Ulf Mattsson, Martyn Hope, Mark Rasch, David Morris
With the new interconnected age comes new risks for cyber attacks and other fraudulent activity. Do you know what you need to keep your end users protected? Digital Insight discusses security and compliance in the interconnected age.
Mobile_Forensics- General Introduction & Software.pptxgouriuplenchwar63
This ppt is related with mobile forensic science where there is general introduction mobile forensics and associated terms. Some information regarding software used in mobile forensics.
Lessons in privacy engineering from a nation scale identity system - connect idDavid Kelts, CIPT
Everybody wants to achieve privacy by design? But how do you do that? This slideshare will show you how. What is privacy? What thought processes will bring about understanding of the security measures to take in order to ensure your users privacy?
This paper examines issues that are impeding the roll-out of smart city initiative and challenges the ideas, ideals and ideology of smart cities as presently conceived.
Citizenship, social justice, and the Right to the Smart Cityrobkitchin
This presentation was delivered at the Right to the Smart City workshop at Maynooth University, Sept 5-6 2017. It sets out a set of questions and theoretical concepts for thinking through issues of citizenship, social justice, and the right to the smart city.
Being a ‘citizen’ in the smart city: Up and down the scaffold of smart citize...robkitchin
This paper discusses the issue of citizens’ participation and rights in the smart city. It does so by drawing on and extending Sherry Arnstein’s seminal work (1969) conceptualising participation in planning and renewal programmes. We argue that citizenship in the smart city is rooted in a pragmatic and paternalistic discourse and practice, rather than in theories around rights and citizenship. Promoters of smart cities, including those advocating a citizen-centric version, tend to conflate limited forms of engagement as a user or consumer of services with citizenship and rights. We develop a modified version of Arnstein’s ladder ― the ‘Scaffold of Smart Citizen Participation’ ― as a conceptual tool to unpack the diverse ways in which the smart city frames citizens and measure smart citizen inclusion, participation, and empowerment in Dublin, Ireland.
Why the National Spatial Strategy failed and prospects for the National Plann...robkitchin
This talk delivered at the MacGill Summer School in Glenties, Donegal as part of a panel on the National Spatial Strategy and where next for spatial planning in Ireland. It sets out the history of spatial planning in Ireland and why the NSS failed and discusses the prospects for a new National Planning Framework
Funding models for open access digital repositoriesrobkitchin
Across jurisdictions and domains (academia, government, business) there has been much recent attention paid to open forms of knowledge production (e.g., open-source software, open data/metadata, open infrastructures) and the creation of open digital repositories for the unrestricted sharing of data, publications and other resources. This paper focuses on the latter, documenting and critically examining 14 different funding streams, grouped into six classes (institutional, philanthropy, research, audience, service, volunteer), being pursued by open digital repositories to support their endeavours, with a particular focus on academic research data repositories. Whilst open digital repositories are free to access, they are not without significant cost to build and maintain, and unstable and cyclical funding poses considerable risks to their futures and the digital collections they hold. While the political and ethical debate concerning the merits of open access and open data is important, we argue that just as salient are concerns with respect to long-term, sustainable funding for the operation and maintenance of open access digital repositories.
Housing in Ireland: From Crisis to Crisisrobkitchin
This paper provides a macro analysis of Irish housing since 1991 and makes the argument that it has been perpetually in crisis and that we need to find a way to treat housing as a sector, not simply a market, and to create stability that will mitigate against boom/bust cycles.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
3. Smart city technologies
Domain Example technologies
Government
E-government systems; online transactions; city operating
systems; performance management systems; urban
dashboards
Security and emergency
services
Centralised control rooms; digital surveillance; predictive
policing; coordinated emergency response
Transport
Intelligent transport systems; integrated ticketing; smart
travel cards; bikeshare; real-time passenger information;
smart parking; logistics management; transport apps
Energy Smart grids; smart meters; energy usage apps; smart
lighting
Waste Compactor bins and dynamic routing/collection
Environment Sensor networks (e.g., pollution, noise, weather; land
movement; flood management)
Buildings Building management systems; sensor networks
Homes Smart meters; app controlled smart appliances
Civic Various apps; open data; volunteered data/hacks
4. Urban big data
• Diverse range of public and private
generation of fine-scale (uniquely
indexical) data about citizens and places in
real-time:
• utilities
• transport providers, logistics systems
• environmental agencies
• mobile phone operators
• app developers
• social media sites
• travel and accommodation websites
• home appliances and entertainment
systems
• financial institutions and retail chains
• private surveillance and security firms
• remote sensing, aerial surveying
• emergency services
• Producing a data deluge that can be
combined, analyzed, acted upon
5. Ethics and security concerns
• Urban big data and smart city
systems create a number of ethical
and security concerns
• Surveillance and erosion privacy
• Ownership, control, data markets
• Social sorting / redlining
• Predictive profiling / anticipatory
governance
• Nudge / behavioural change
• Dynamic pricing
• Data security
• Control creep
• Buggy, brittle, hackable urban
systems
6. Privacy
• Privacy debates concern acceptable practices
with regards to accessing and disclosing
personal and sensitive information about a
person
• identity privacy (to protect personal and
confidential data)
• bodily privacy (to protect the integrity of
the physical person);
• territorial privacy (to protect personal
space, objects and property);
• locational and movement privacy (to
protect against the tracking of spatial
behaviour)
• communications privacy (to protect against
the surveillance of conversations and
correspondence);
• transactions privacy (to protect against
monitoring of queries/searches, purchases,
and other exchanges)
7. A Taxonomy of Privacy Harms (compiled from Solove 2006)
Domain Privacy breach Description
Information
Collection
Surveillance Watching, listening to, or recording of an individual’s activities
Interrogation Various forms of questioning or probing for information
Information
Processing
Aggregation The combination of various pieces of data about a person
Identification Linking information to particular individuals
Insecurity Carelessness in protecting stored information from leaks and
improper access
Secondary Use Use of information collected for one purpose for a different
purpose without the data subject’s consent
Exclusion Failure to allow the data subject to know about the data that others
have about her and participate in its handling and use, including
being barred from being able to access and correct errors
Information
Dissemination
Breach of Confidentiality Breaking a promise to keep a person’s information confidential
Disclosure Revelation of information about a person that impacts the way
others judge her character
Exposure Revealing another’s nudity, grief, or bodily functions
Increased Accessibility Amplifying the accessibility of information
Blackmail Threat to disclose personal information
Appropriation The use of the data subject’s identity to serve the aims and
interests of another
Distortion Dissemination of false or misleading information about individuals
Invasion Intrusion Invasive acts that disturb one’s tranquillity or solitude
Decisional Interference Incursion into the data subject’s decisions regarding her private
affairs
8. Privacy and big urban data
• Intensifies datafication
• The capture and circulation
data are:
• indiscriminate and exhaustive
(involve all individuals, objects,
transactions, etc.);
• distributed (occur across
multiple devices, services and
places);
• platform independent (data can
flow across platforms, services,
and devices);
• continuous (data are generated
on a routine and automated
basis).
9. Location/movement data
• Controllable digital CCTV cameras + ANPR + facial
recognition
• Smart phones: cell masts, GPS, wifi
• Sensor networks: capture and track phone identifiers
such as MAC addresses
• Wifi mesh: capture & track phones with wifi turned on
• Smart card tracking: barcodes/RFID chips (buildings &
public transport)
• Vehicle tracking: unique ID transponders for automated
road tolls & car parking
• Other staging points: ATMs, credit card use, metadata
tagging
• Electronic tagging; shared calenders
10. Data type Data permissions that can be sought by android apps (from Hein 2014)
Accounts log email log
App Activity name, package name, process number of activity, processed id
App Data Usage Cache size, code size, data size, name, package name
App Install installed at, name, package name, unknown sources enabled, version code, version
name
Battery health, level, plugged, present, scale, status, technology, temperature, voltage
Device Info board, brand, build version, cell number, device, device type, display, fingerprint, IP,
MAC address, manufacturer, model, OS platform, product, SDK code, total disk
space, unknown sources enabled
GPS accuracy, altitude, latitude, longitude, provider, speed
MMS from number, MMS at, MMS type, service number, to number
NetData bytes received, bytes sent, connection type, interface type
PhoneCall call duration, called at, from number, phone call type, to number
SMS from number, service number, SMS at, SMS type, to number
TelephonyInfo cell tower ID, cell tower latitude, cell tower longitude, IMEI, ISO country code, local
area code, MEID, mobile country code, mobile network code, network name,
network type, phone type, SIM serial number, SIM state, subscriber ID
WifiConnection BSSID, IP, linkspeed, MAC addr, network ID, RSSI, SSID
WifiNeighbors BSSID, capabilities, frequency, level, SSID
Root Check root status code, root status reason code, root version, sig file version
Malware Info algorithm confidence, app list, found malware, malware SDK version, package list,
reason code, service list, sigfile version
11. Privacy and big urban data
• Intensifies datafication
• Deepens inferencing and
predictive privacy harms
• Weak anonymization and enables
re-identification
• Opacity and automation creates
obfuscation and reduces control
• Data are being shared and
repurposed and used in
unpredictable and unexpected
ways
• Notice and consent is an empty
exercise or absent
•
12. R
Fair Information Practice Principles (OECD, 1980)
Principle Description
Notice Individuals are informed that data are being generated and the
purpose to which the data will be put
Choice Individuals have the choice to opt-in or opt-out as to whether and
how their data will be used or disclosed
Consent Data are only generated and disclosed with the consent of
individuals
Security Data are protected from loss, misuse, unauthorized access,
disclosure, alteration and destruction
Integrity Data are reliable, accurate, complete and current
Access Individuals can access, check and verify data about themselves
Use Data are only used for the purpose for which they are generated
and individuals are informed of each change of purpose
Accountability The data holder is accountable for ensuring the above principles
and has mechanisms in place to assure compliance
Redundant in the age of big urban data?
13. Privacy and big urban data
• Intensifies datafication
• Deepens inferencing and
predictive privacy harms
• Weak anonymization and enables
re-identification
• Opacity and automation creates
obfuscation and reduces control
• Data are being shared and
repurposed and used in
unpredictable and unexpected
ways
• Notice and consent is an empty
exercise or absent
• Data security
14. Hacking the city
• Weak security and encryption
• Insecure legacy systems and poor
maintenance
• Large and complex attack surfaces
and interdependencies
• Cascade effects
• Human error and disgruntled
(ex)employees
15. Suggested solutions
• Market:
• Industry standards and self-regulation
• Privacy/security as competitive advantage
• Technological
• End-to-end strong encryption, access controls, security controls, audit
trails, backups, up-to-date patching, etc.
• Privacy enhancement tools
• Policy and regulation
• FIPPs
• Privacy by design;
• security by design
• Governance
• Vision and strategy: (1) smart city advisory board and smart city strategy;
• Oversight of delivery and compliance: (2) smart city governance, risk and
compliance board;
• Day-to-day delivery: (3) core privacy/security team, smart city
privacy/security assessments, and (4) computer emergency response team
