In this webinar we discuss privacy, it's relevance to data science, and how privacy-preserving synthetic data can help organizations build a bridge between compliance and efficient use of data.
CIKM2020 Keynote: Accelerating discovery science with an Internet of FAIR dat...Michel Dumontier
Biomedicine has always been a fertile and challenging domain for computational discovery science. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies, offer exciting opportunities to reuse our collective knowledge, were we not stymied by incompatible formats, overlapping and incomplete vocabularies, unclear licensing, and heterogeneous access points. In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
bio:
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research focuses on the development of computational methods for scalable and responsible discovery science. Dr. Dumontier obtained his BSc (Biochemistry) in 1998 from the University of Manitoba, and his PhD (Bioinformatics) in 2005 from the University of Toronto. Previously a faculty member at Carleton University in Ottawa and Stanford University in Palo Alto, Dr. Dumontier founded and directs the interfaculty Institute of Data Science at Maastricht University to develop sociotechnological systems for responsible data science by design. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon 2020, the European Open Science Cloud, the US National Institutes of Health and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This presentation was given on October 21, 2020 at CIKM2020.
Digital Enterprise Festival Birmingham 13/04/17 - Ian West Cognizant VP Data ...CIO Edge
Learn what the EU Global Data Protection Regulation means for your business – Carrot or Stick its your choice but with fines of €20m or up to 4% of Global Revenue (whichever is the larger) being applied for every data breach and every data mis-use after May 2018 the carrot is the better option.
Are you aware? Are you prepared? Do you comply?
To book a free non sales consultation about GDPR with Ian West contact us enquiry@digitalenterprisefest.com
Privacy Regulations and Your Digital SetupPiwik PRO
How Will the New Privacy Regulations Affect Your Digital Set-up? In less than 2 years from now, Europe’s new data privacy law will come into effect, changing the way organizations handle information of their users. General Data Protection Regulation will heavily impact usage of digital tools for customer insights and analytics.
This presentation was created by the Piwik PRO Team for a webinar session with Aurelie Pols. Webinar recording is available on: https://youtu.be/dPOvbbZ3vdo
Data privacy awareness is on the rise. Users become more and more concerned with how online service providers collect and protect their personal information. And so should you. Discover how to balance the risks and benefits of collecting data in the age of customer centricity.
Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innova...Denodo
Watch full webinar here: https://bit.ly/36GEuJO
Traditional data integration is falling short to meet new business requirements - real-time connected data, self-service, automation, speed, and intelligence. Forrester analyst will explain how data fabric is emerging as a hot new market for an intelligent and unified platform.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
CIKM2020 Keynote: Accelerating discovery science with an Internet of FAIR dat...Michel Dumontier
Biomedicine has always been a fertile and challenging domain for computational discovery science. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies, offer exciting opportunities to reuse our collective knowledge, were we not stymied by incompatible formats, overlapping and incomplete vocabularies, unclear licensing, and heterogeneous access points. In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
bio:
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research focuses on the development of computational methods for scalable and responsible discovery science. Dr. Dumontier obtained his BSc (Biochemistry) in 1998 from the University of Manitoba, and his PhD (Bioinformatics) in 2005 from the University of Toronto. Previously a faculty member at Carleton University in Ottawa and Stanford University in Palo Alto, Dr. Dumontier founded and directs the interfaculty Institute of Data Science at Maastricht University to develop sociotechnological systems for responsible data science by design. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon 2020, the European Open Science Cloud, the US National Institutes of Health and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This presentation was given on October 21, 2020 at CIKM2020.
Digital Enterprise Festival Birmingham 13/04/17 - Ian West Cognizant VP Data ...CIO Edge
Learn what the EU Global Data Protection Regulation means for your business – Carrot or Stick its your choice but with fines of €20m or up to 4% of Global Revenue (whichever is the larger) being applied for every data breach and every data mis-use after May 2018 the carrot is the better option.
Are you aware? Are you prepared? Do you comply?
To book a free non sales consultation about GDPR with Ian West contact us enquiry@digitalenterprisefest.com
Privacy Regulations and Your Digital SetupPiwik PRO
How Will the New Privacy Regulations Affect Your Digital Set-up? In less than 2 years from now, Europe’s new data privacy law will come into effect, changing the way organizations handle information of their users. General Data Protection Regulation will heavily impact usage of digital tools for customer insights and analytics.
This presentation was created by the Piwik PRO Team for a webinar session with Aurelie Pols. Webinar recording is available on: https://youtu.be/dPOvbbZ3vdo
Data privacy awareness is on the rise. Users become more and more concerned with how online service providers collect and protect their personal information. And so should you. Discover how to balance the risks and benefits of collecting data in the age of customer centricity.
Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innova...Denodo
Watch full webinar here: https://bit.ly/36GEuJO
Traditional data integration is falling short to meet new business requirements - real-time connected data, self-service, automation, speed, and intelligence. Forrester analyst will explain how data fabric is emerging as a hot new market for an intelligent and unified platform.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
How financial organizations can use synthetic data to overcome data inertia |...Statice
This presentation details a way for banks and financial institutions to regain their ability to work with data safely and efficiently. It shows how privacy-preserving synthetic data helps enterprises gain agility in data operations while complying with the financial industry's data constraints.
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Creating a Healthcare Data Fabric, and Providing a Single, Unified, and Curat...Denodo
Watch full webinar here: https://bit.ly/32jYpiD
Data fragmentation, multiple data sources and interoperability are a significant part of the challenges facing modern healthcare. We will focus our session on how to address these through a combination of a Universal Healthcare Data Fabric that leverages Denodo’s latest platform, as well as components that have been developed specifically for healthcare systems, based on the FHIR standard.
Trivadis TechEvent 2016 Big Data Privacy and Security Fundamentals by Florian...Trivadis
In Big Data we focus on the 4 V's: Volume, Velocity, Varity and Veracity. But another important topic is often not in the focus: Privacy and Security. Yet as important and if not considered from the beginning it might put your Big Data project at risk. Learn about most important Privacy and Security fundamentals in Big Data, you should take into account in your next Big Data project.
Big Data Ecosystem for Data-Driven Decision MakingAbzetdin Adamov
The extremely fast grow of Internet Services, Web and Mobile Applications and advance of the related Pervasive, Ubiquity and Cloud Computing concepts have stumulated production of tremendous amounts of data partially available online (call metadata, texts, emails, social media updates, photos, videos, location, etc.). Even with the power of today’s modern computers it still big challenge for business and government organizations to manage, search, analyze, and visualize this vast amount of data as information. Data-Intensive computing which is intended to address this problems become quite intense during the last few years yielding strong results. Data intensive computing framework is a complex system which includes hardware, software, communications, and Distributed File System (DFS) architecture.
Just small part of this huge amount is structured (Databases, XML, logs) or semistructured (web pages, email), over 90% of this information is unstructured, what means data does not have predefined structure and model. Generally, unstructured data is useless unless applying data mining and analysis techniques. At the same time, just in case if you can process and understand your data, this data worth anything, otherwise it becomes useless.
Data Standardization with Web Data Integration PromptCloud
Before analyzing data aggregated from multiple sources, it is essential to first standardize the datasets. At PromptCloud, we put special emphasis on this process and understand that as a web crawling company, our solution must enable our clients to integrate data efficiently.
Product Keynote: Denodo 8.0 - A Logical Data Fabric for the Intelligent Enter...Denodo
Watch full webinar here: https://bit.ly/2O9gcBT
Denodo 8 expands data integration and management to data fabric with advanced data virtualization capabilities. What are they? Denodo CTO Alberto Pan will touch upon the key Denodo 8 capabilities.
Big Data Solutions, Big Data Services | V2SoftV2Soft
V2Soft provides advanced integrated customized Big Data Infrastructure Management Solutions, Application Development, Analytics services across domains which help customers maximize revenue and increase operational efficiency.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
Digital Transformation: How to Build an Analytics-Driven CultureAlexander Loth
http://alexloth.com/2017/12/11/diversify-long-term-crypto-portfolio/
<- Follow-up blog post "How to diversify a Long-term Crypto Portfolio"!
Executive Talk, Frankfurt School of Finance & Management, 8 December 2017
Big Data LDN 2018: REALISING THE PROMISE OF SELF-SERVICE ANALYTICS WITH DATA ...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:50 - 12:20
Speaker: Mark Pritchard
Organisation: Denodo
About: Self-service analytics promises to liberate business users to perform analytics without the assistance of IT, and this in turn promises to free IT to focus on enhancing the infrastructure.
Join us to learn how data virtualization will allow you to gain real-time access to enterprise-wide data and deliver self-service analytics. We will explore how you can seamlessly unify fragmented data, replace your high-maintenance and high cost data integrations with a single, low-maintenance data virtualization layer; and how you can preserve your data integrity and ensure data lineage is fully traceable.
Shift AI 2020: Business benefits of privacy-preserving synthetic data | Sebas...Shift Conference
Shift AI was a success, connecting hundreds of professionals that were eager to propel the progress of AI and discuss the newest technologies in data mining, machine learning and neural networks. More at https://ai.shiftconf.co/.
Talk description:
Privacy defines a state in which one is free from public attention and not observed or disturbed by others. Taken in the context of data, privacy is therefore a state in which an individual’s data is used only with their specific consent, and where any person or organization party to that individual’s data guarantee to prevent unauthorized disclosures or misuse of that information.
Therefore, in order to protect the individual's privacy, strict regulations have already been introduced in many regions and countries worldwide, such as CCPA in California or GDPR in the EU and we can expect many more to come. This puts businesses in a position in which they need to find a solution in order to leverage data while preserving privacy. We will address this topic and answer how businesses can benefit from synthetic data and unlock the value of data.
How financial organizations can use synthetic data to overcome data inertia |...Statice
This presentation details a way for banks and financial institutions to regain their ability to work with data safely and efficiently. It shows how privacy-preserving synthetic data helps enterprises gain agility in data operations while complying with the financial industry's data constraints.
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Creating a Healthcare Data Fabric, and Providing a Single, Unified, and Curat...Denodo
Watch full webinar here: https://bit.ly/32jYpiD
Data fragmentation, multiple data sources and interoperability are a significant part of the challenges facing modern healthcare. We will focus our session on how to address these through a combination of a Universal Healthcare Data Fabric that leverages Denodo’s latest platform, as well as components that have been developed specifically for healthcare systems, based on the FHIR standard.
Trivadis TechEvent 2016 Big Data Privacy and Security Fundamentals by Florian...Trivadis
In Big Data we focus on the 4 V's: Volume, Velocity, Varity and Veracity. But another important topic is often not in the focus: Privacy and Security. Yet as important and if not considered from the beginning it might put your Big Data project at risk. Learn about most important Privacy and Security fundamentals in Big Data, you should take into account in your next Big Data project.
Big Data Ecosystem for Data-Driven Decision MakingAbzetdin Adamov
The extremely fast grow of Internet Services, Web and Mobile Applications and advance of the related Pervasive, Ubiquity and Cloud Computing concepts have stumulated production of tremendous amounts of data partially available online (call metadata, texts, emails, social media updates, photos, videos, location, etc.). Even with the power of today’s modern computers it still big challenge for business and government organizations to manage, search, analyze, and visualize this vast amount of data as information. Data-Intensive computing which is intended to address this problems become quite intense during the last few years yielding strong results. Data intensive computing framework is a complex system which includes hardware, software, communications, and Distributed File System (DFS) architecture.
Just small part of this huge amount is structured (Databases, XML, logs) or semistructured (web pages, email), over 90% of this information is unstructured, what means data does not have predefined structure and model. Generally, unstructured data is useless unless applying data mining and analysis techniques. At the same time, just in case if you can process and understand your data, this data worth anything, otherwise it becomes useless.
Data Standardization with Web Data Integration PromptCloud
Before analyzing data aggregated from multiple sources, it is essential to first standardize the datasets. At PromptCloud, we put special emphasis on this process and understand that as a web crawling company, our solution must enable our clients to integrate data efficiently.
Product Keynote: Denodo 8.0 - A Logical Data Fabric for the Intelligent Enter...Denodo
Watch full webinar here: https://bit.ly/2O9gcBT
Denodo 8 expands data integration and management to data fabric with advanced data virtualization capabilities. What are they? Denodo CTO Alberto Pan will touch upon the key Denodo 8 capabilities.
Big Data Solutions, Big Data Services | V2SoftV2Soft
V2Soft provides advanced integrated customized Big Data Infrastructure Management Solutions, Application Development, Analytics services across domains which help customers maximize revenue and increase operational efficiency.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
Digital Transformation: How to Build an Analytics-Driven CultureAlexander Loth
http://alexloth.com/2017/12/11/diversify-long-term-crypto-portfolio/
<- Follow-up blog post "How to diversify a Long-term Crypto Portfolio"!
Executive Talk, Frankfurt School of Finance & Management, 8 December 2017
Big Data LDN 2018: REALISING THE PROMISE OF SELF-SERVICE ANALYTICS WITH DATA ...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:50 - 12:20
Speaker: Mark Pritchard
Organisation: Denodo
About: Self-service analytics promises to liberate business users to perform analytics without the assistance of IT, and this in turn promises to free IT to focus on enhancing the infrastructure.
Join us to learn how data virtualization will allow you to gain real-time access to enterprise-wide data and deliver self-service analytics. We will explore how you can seamlessly unify fragmented data, replace your high-maintenance and high cost data integrations with a single, low-maintenance data virtualization layer; and how you can preserve your data integrity and ensure data lineage is fully traceable.
Shift AI 2020: Business benefits of privacy-preserving synthetic data | Sebas...Shift Conference
Shift AI was a success, connecting hundreds of professionals that were eager to propel the progress of AI and discuss the newest technologies in data mining, machine learning and neural networks. More at https://ai.shiftconf.co/.
Talk description:
Privacy defines a state in which one is free from public attention and not observed or disturbed by others. Taken in the context of data, privacy is therefore a state in which an individual’s data is used only with their specific consent, and where any person or organization party to that individual’s data guarantee to prevent unauthorized disclosures or misuse of that information.
Therefore, in order to protect the individual's privacy, strict regulations have already been introduced in many regions and countries worldwide, such as CCPA in California or GDPR in the EU and we can expect many more to come. This puts businesses in a position in which they need to find a solution in order to leverage data while preserving privacy. We will address this topic and answer how businesses can benefit from synthetic data and unlock the value of data.
ISC2 Privacy-Preserving Analytics and Secure Multiparty ComputationUlfMattsson7
Use Cases in Machine learning (ML)
Secure Multi-Party Computation (SMPC)
Homomorphic encryption (HE)
Differential Privacy (DP) and K-Anonymity
Pseudonymization and Anonymization
Synthetic Data
Zero trust architecture (ZTA)
Zero-knowledge proofs (ZKP)
Private Set Intersection (PSI)
Trusted execution environments (TEE)
Post-Quantum Cryptography
Regulations and Standards in Data Privacy
Protecting data privacy in analytics and machine learning ISACA London UKUlf Mattsson
ISACA London Chapter webinar, Feb 16th 2021
Topic: “Protecting Data Privacy in Analytics and Machine Learning”
Abstract:
In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to put these technologies to work for databases and other data sources.
When we think about developing AI responsibly, there’s many different activities that we need to think about.
This session also discusses international standards and emerging privacy-enhanced computation techniques, secure multiparty computation, zero trust, cloud and trusted execution environments. We will discuss the “why, what, and how” of techniques for privacy preserving computing.
We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud.
We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.
Privacy preserving computing and secure multi-party computation ISACA AtlantaUlf Mattsson
A major challenge that many organizations faces, is how to address data privacy regulations such as CCPA, GDPR and other emerging regulations around the world, including data residency controls as well as enable data sharing in a secure and private fashion. We will present solutions that can reduce and remove the legal, risk and compliance processes normally associated with data sharing projects by allowing organizations to collaborate across divisions, with other organizations and across jurisdictions where data cannot be relocated or shared.
We will discuss secure multi-party computation where organizations want to securely share sensitive data without revealing their private inputs. We will review solutions that are driving faster time to insight by the use of different techniques for privacy-preserving computing including homomorphic encryption, k-anonymity and differential privacy. We will present best practices and how to control privacy and security throughout the data life cycle. We will also review industry standards, implementations, policy management and case studies for hybrid cloud and on-premises.
Safeguarding customer and financial data in analytics and machine learningUlf Mattsson
Digital Transformation and the opportunities to use data in Analytics and Machine Learning are growing exponentially, but so too are the business and financial risks in Data Privacy. The increasing number of privacy incidents and data breaches are destroying brands and customer trust, and we will discuss how business prioritization can be benefit from a finance-based data risk assessment (FinDRA).
More than 60 countries have introduced privacy laws and by 2023, 65% of the world’s population will have its personal information covered under modern privacy regulations. We will discuss use cases in financial services that are finding a balance between new technology impact, regulatory compliance, and commercial business opportunity. Several privacy-preserving and privacy-enhanced techniques can provide practical security for data in use and data sharing, but none universally cover all use cases. We will discuss what tools can we use mitigate business risks caused by security threats, data residency and privacy issues. We will discuss how technologies like pseudonymization, anonymization, tokenization, encryption, masking and privacy preservation in analytics and business intelligence are used in Analytics and Machine Learning.
Organizations are increasingly concerned about data security in processing personal information in external environments, such as the cloud; and information sharing. Data is spreading across hybrid IT infrastructure on-premises and multi-cloud services and we will discuss how to enforce consistent and holistic data security and privacy policies. Increasing numbers of data security, privacy and identity access management products are in use, but they do not integrate, do not share common policies, and we will discuss use cases in financial services of different techniques to protect and manage data security and privacy.
Are you aware that 69% of CXOs have identified data loss from older employees who leave as a major problem? A recent Osterman Research survey identified this as a significant issue for organizations.
When employees depart, they often take sensitive and confidential data with them intentionally or inadvertently, which can cause significant risks for your organization.
Protecting the emails and files of former employees is essential to minimize these risks, ensure compliance, support legal readiness, promote knowledge reuse, and enable smooth role transitions.
How Insurers Fueled Transformation During a PandemicNuxeo
For many insurers, the past year has accelerated strategic investments to manage remote workforces, support virtual claims handling, and face off with FinTech upstarts.
In this webinar, we look at how leading insurers not only addressed the immediate challenges caused by global lockdowns but also found new efficiencies along the way. Get insights into some of the emerging technologies that are driving innovation in insurance, including the Cloud, artificial intelligence, and low-code. We also explore how these technologies reduce claims leakage while improving claims accuracy, employee productivity, and customer satisfaction.
Presentation on key legal issues regarding use and developments of BOTs, AI - GDPR, Data Protection. Case study BRISbot. Presentation delivered at Epicenter 30 of May 2017 in partnership with BRIS and Microsoft.
In the insurance industry, the advantage of custom-built marts and warehouses ensures that the structure and queries match the data, but the customization makes it very difficult and expensive to maintain. On the other hand, off-the-shelf marts and warehouses maintained by the third party and are general and less useful than the custom ones. In either case, they can easily grow beyond anything manageable. This whitepaper focuses on providing an overview of data warehousing in the insurance industry.
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
In this webinar factsheet, SAP’s Rohit Nagarajan and Suni Verma from Ernst & Young explore Big Data in India, adoption patterns across the globe, and how you can embark on your own Big Data journey.
D2 d turning information into a competive asset - 23 jan 2014Henk van Roekel
Understanding the evolution of Business Intelligence and Analytics and the challenges and opportunities that come with it. Exploring CGI's Data2Diamonds™ approach ensuring financial sound, technical viable and socially desirable Big Data initiatives.
Date: 15th November 2017
Location: AI Lab Theatre
Time: 16:30 - 17:00
Speaker: Elisabeth Olafsdottir / Santiago Castro
Organisation: Microsoft / Keyrus
Big Data is the lastest cashcow. Data Analytics has now a crucial role for industries. This article describes as to what is Big Data and Analytics and how a Chartered Accountant will be able to provide value in this field.
Similar to How businesses can benefit from privacy preserving synthetic data (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
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How businesses can benefit from privacy preserving synthetic data
1. Statice Webinar
How can businesses benefit from
privacy-preserving synthetic data?
Berlin 2020
2. Statice Webinar | 2020
Outline
1. What is privacy?
2. Data sharing
a. Why share data?
b. Data sharing done wrong
c. Synthetic data as a solution
3. What can you do with synthetic data?
4. Customer cases
5. Q+A
4. Statice Webinar | 2020
● English dictionary definition:
“Privacy is a state in which one is
not observed or disturbed by other
people”
● Lack of privacy => behavioral
change
● Privacy is fundamental to a free
society
Anonymous voting guarantees
freedom of choice
Privacy landscape
6. Statice Webinar | 2020
Privacy in the present
● Digital tracking
everywhere
● Social circle, browsing
habits, shopping details,
location tracking, emails,
calls ...
7. Statice Webinar | 2020
Data protection regulations
● Protection of individual privacy
● Over 80 countries and regions
worldwide
● Strictest regulation
○ GDPR - European Union (2018)
● High fines for violations
https://termly.io/resources/infographics/privacy-laws-around-the-world/
8. Use of sensitive data in your company made practically
impossible because of data protection regulations:
Your data teams are slowed down as data is
generally accessible only after a long
governance process
Your production data cannot be stored or
processed using cloud resources as customer
consent is mostly not feasible for exploratory
data analysis.
Your production data cannot be shared
with partners for product development or
research.
Statice Webinar | 2020
9. Statice Webinar | 2020
Privacy promise: Opt-out scenario
● My data must have no
effect on any analysis
carried on on the dataset
● Problem: if nobody’s data
has no effect on any
analysis then there will be
no utility.
10. Statice Webinar | 2020
Privacy promise:
what can we expect?
● A tradeoff
○ With or without my data,
any outcome of any
analysis should be the
same
○ The impact on me sharing
information in the dataset
will be limited to the general
learnings not the specifics
of my information
12. Statice Webinar | 2020
Why share data?
● As individuals, we share data all the
time
○ With our doctors
○ With our accountants
○ In exchange for a trusted
service
● Privacy is not necessarily complete
non-disclosure
13. Statice Webinar | 2020
Why share data?
● Society benefits from individuals
sharing their data
○ Medical advances
○ Sociological research,
understanding society dynamics
● Examples:
○ Tracking commute patterns to
improve public transport
networks
○ Detect epidemia and act fast by
looking at search engine disease
queries/medicine orders
18. Statice Webinar | 2020
Illustration: Cambridge Analytica
● Infamous leak involved Personally Identifiable Information of over 50
million people
https://www.theguardian.com/technology/2018/mar/17/facebook-cambridge-analytica-kogan-data-algorithm
19. Statice Webinar | 2020
Information not unique to you: "quasi-identifiers"
20. Statice Webinar | 2020
Illustration: Massachusetts Governor leak
Sweeney, Latanya. Weaving Technology and Policy Together to Maintain
Confidentiality. Journal of Law, Medicine and Ethics, Vol. 25 1997, p. 98-110
21. Statice Webinar | 2020
Fingerprint-like information
● On its own, a fingerprint
seems cryptic
● Around 100 minutiae in a
fingerprint
● Experts declare a fingerprint
match if 12 minutiae match
● Precise identification is
possible if fingerprints are
indexed and queryable
22. Statice Webinar | 2020
Illustration: Netflix movie preferences
Join movie
ratings
Ratings of only 4-5 movies
allowed successful
identification of a large
number of users was
possible.
Narayanan A, Shmatikov V. Robust de-anonymization of large spa
datasets. InSecurity and Privacy, 2008. SP 2008. IEEE Symposium on
2008 May 18 (pp. 111-125). IEEE.
23. Statice Webinar | 2020
French Military Base in MaliHeatmap 30 million runners
worldwide
Not that many in the
Sahara
Illustration: Strava Running Tracks
24. Statice Webinar | 2020
And many more . . .
● Search queries
● Browser configuration
25. So how do we
enable the use of
sensitive customer
data while staying
privacy-compliant?
Statice Webinar | 2020
26. Recital 26 of the GDPR:
“This regulation does not therefore concern the processing of such
anonymous information, including for statistical or research
purposes.”
The best way to securely access and leverage sensitive customer
data is to use anonymous data.
Statice Webinar | 2020
27. The problem is that traditional
anonymization methods are unable
to preserve the granularity and
quality of the original data required
for further processing and analysis.
Either they obfuscate data to a large
extent or they do not properly protect
the data.
Data utility Data privacy
vs.
Statice Webinar | 2020
29. Statice is a data anonymization
engine that enables the secure
anonymization of data while
preserving its statistical utility and
data structure.
This allows you to perform meaningful
data analysis without ever exposing
the original data.
Statice Webinar | 2020
30. Guaranteed data privacy
Statice generates
privacy-preserving synthetic
data which is based on
mathematical privacy
guarantees.
Data anonymization made easy.
Automatic anonymization
and granular data quality
Statice anonymizes your
data preserving statistical
utility and data structure by
generating synthetic data.
Flexible integration
Statice can be conveniently
used on-premise both via a
CLI or as a Python library.
Support for all
structured data
Statice supports the
anonymization of tabular,
relational, time-series,
geolocation and other types
of structured data.
Statice Webinar | 2020
31. Original
data Statice
engine
Anonymous
synthetic
data
1 2 3
Data analysis
● Automatic understanding
of provided data types
● Automatic data
classification
Training
● Generative algorithms
learn the statistical
structure and information
of the original data
Data generation
● Generation of anonymous
synthetic data
● Provision of automatic
utility and risk evaluations
How Statice works
Statice Webinar | 2020
32. Automatic
evaluation metrics
that are part of the
Statice software
prove how the
statistical
properties of the
original data are
preserved in the
newly-generated
anonymous
synthetic data.
Statice Webinar | 2020
34. Use data protection to
your advantage and
get the most value out
of your data
Build your data sandbox
Train your machine learning algorithms
Protect your customer data for BI analysis
Enable your scalable use of cloud infrastructures
Use Statice to effectively protect sensitive data in order to
share it easily with partners or across your organization for
quick access and collaborative use.
Leverage synthetic data by Statice to train your machine
learning models with the same accuracy as when using
real-world data.
By anonymizing customer data directly, you add a strong
safeguard for protecting your customers and enable quick
and flexible data analysis.
Process synthetic data in cloud instances without ever
putting sensitive data at risk and yet benefit from a scalable
infrastructure and the cost-efficient use of cloud resources
for your company.
Statice Webinar | 2020
36. Customer case 1:
The Statice engine
enabling a German
insurance provider to
tailor products to its
customers
Challenges
● Impeded timely access to data and availability of granular
information because of legal constraints
● Complicated product development due to sensitive customer
data and privacy regulations
● Biased customer behavior modeling due to lack of access to
complete customer data sets
● Weeks/months period between customer data acquisition and
data processing
Solutions
● Enabled timely access to data with Statice by generating
synthetic data based on real customer data
● Creation of anonymous data warehouse with much lower
compliance hurdles to allow data science teams to work faster on
more representative data
Long-term benefits
● Unlock sensitive customer data as a prime resource for product
innovation
● Massively reduced time-to-data for both internal and external
stakeholders (weeks/months to days)
● Lowered compliance overhead and enable innovation
prototyping
Statice Webinar | 2020
37. ● High risk of engaging in collaborative partnerships due
to sensitive customer data exchange processes
● Potential exposure to customer data leakage and its
legal implications
● Reduced ability to devise innovative strategies with third
parties due to data privacy and security concerns
Solutions
● Statice implemented to produce privacy-preserving
synthetic data
● Safe data, with much lower compliance hurdles for
partnerships, created for external sharing
Long-term benefits
● Compliant and collaborative product development &
data monetisation
● Facilitated innovative partnerships through
unconstrained customer data exchange
Customer case 2:
The Statice engine
allowing a German
healthcare enterprise
to safely engage in
collaborative
partnerships
Statice Webinar | 2020
Challenges
38. Customer case 3:
The Statice engine
enabling a German
telecommunications
company the scalable
use of cloud
infrastructure
● Hugely valuable data in the business’ data exhaust
which cannot be properly exploited due to privacy
concerns
● Inability to scale a data processing and analysis pipeline
on cloud infrastructure due to sensitive data exposure
● High costs and major delays in innovation projects due
to the incapacity to perform and scale data processing
on the cloud infrastructure
Solutions
● Use customer data in the form of privacy-compliant
synthetic data which contains highly similar statistical
information
● Use of synthetic data generated with Statice offers the
freedom to freely, cost-efficiently, fast and safely scale
solutions on cloud infrastructure without concerns
around customer data privacy
Long-term benefits
● Accelerated, cost-efficient use of cloud resources and
data for software testing
Statice Webinar | 2020
Challenges
39. Statice ensures full
data privacy
compliance allowing
your data team to
work more efficiently
Using Statice you can:
Minimize your time-to-data from months to days.
Unlock your sensitive customer data as a prime
resource for product innovation.
Ensure your regulatory compliance for the whole
data value chain.
Statice Webinar | 2020
41. Unlock your data
with Statice.
ben@statice.ai
statice.ai
Ben Nolan
Head of Business Development
42. Statice Webinar | 2020
Are you interested in learning
more about working with us?
43. 3. Project kick-off2. Technical planning1. Feasibility study
~8 weeks
WE FOLLOW THREE STEPS ON THE WAY TO A COOPERATION
Goal
Involved
parties
Results
Understanding scope of data
and use case for the customer
Successful planning of the
infrastructure to be used
Successful coordination of
joint project plan
● Evaluation of shared
data schema
● Implementation plan
● Infrastructure plan
● Joint project plan
● Date for project start
& the customer & the customer & the customer
Statice Webinar | 2020