Bio cv ulf mattsson
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Mr. Mattsson is currently the Head of Innovation at TokenEx, a cloud-based data security company, was previously Chief Technology Officer at Atlantic BT Security Solutions, and earlier Chief Technology Officer at Compliance Engineering. He was the Chief Technology Officer and a technology founder of Protegrity. Prior to Protegrity, he worked 20 years at IBM's Research and Development organization, in the areas of Application and Database Security. He also worked at companies providing Data Discovery Services, Cloud Application Security Brokers, Web Application Firewalls, Managed Security Service, Security Operation Center, and Cybersecurity consulting. Mr. Mattsson is an Inventor of 73 Awarded and Issued US Patents. He delivered joint Application and Data Protection products and development teams at IBM, Microsoft, Hewlett-Packard, Oracle, Teradata, and RSA Security (Dell). Mr. Mattsson is a also advising companies in the area of AI, Machine Learning and Quantum Computing technologies. Mr. Mattsson also owns and manages the BrightTALK “Cybersecurity - The No Spin Zone” and “The Blockchain Channel.”
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Unicode is an information technology standard for the consistent encoding, representation, and handling of text expressed in most of the world's writing systems. The standard is maintained by the Unicode Consortium, and as of March 2020, it has a total of 143,859 characters, with Unicode 13.0 (these characters consist of 143,696 graphic characters and 163 format characters) covering 154 modern and historic scripts, as well as multiple symbol sets and emoji. The character repertoire of the Unicode Standard is synchronized with ISO/IEC 10646, each being code-for-code identical with the other.
The Unicode Standard consists of a set of code charts for visual reference, an encoding method and set of standard character encodings, a set of reference data files, and a number of related items, such as character properties, rules for normalization, decomposition, collation, rendering, and bidirectional text display order (for the correct display of text containing both right-to-left scripts, such as Arabic and Hebrew, and left-to-right scripts). Unicode's success at unifying character sets has led to its widespread and predominant use in the internationalization and localization of computer software. The standard has been implemented in many recent technologies, including modern operating systems, XML, Java (and other programming languages), and the .NET Framework.
Unicode can be implemented by different character encodings. The Unicode standard defines Unicode Transformation Formats (UTF) UTF-8, UTF-16, and UTF-32, and several other encodings. The most commonly used encodings are UTF-8, UTF-16, and UCS-2 (a precursor of UTF-16 without full support for Unicode)
The future of data security and blockchain
Discussion of Post-Quantum Cryptography and other technologies:
Data Security Techniques
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
Blockchain
Regulations and Standards in Data Privacy
GDPR and evolving international privacy regulations
Convergence of data privacy principles, standards and regulations
General Data Protection Regulation (GDPR)
GDPR and California Consumer Privacy Act (CCPA)
What role does technologies play in compliance
Use Cases
Privacy preserving computing and secure multi-party computation ISACA Atlanta
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 learning
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.
Protecting data privacy in analytics and machine learning ISACA London UK
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.
New opportunities and business risks with evolving privacy regulations
In the shadow of the global pandemic and the associated economic downturn, organizations are focused on cost optimization, which often leads to impulsive decisions to deprioritize compliance with all nonrevenue programs.
Regulators have evolved to adapt with the notable increase in data subject complaints and are getting more serious about organizations that don’t properly protect consumer data. Marriott was hit with a $124 million fine while Equifax agreed to pay a minimum of $575 million for its breach. The US Federal Trade Commission, the US Consumer Financial Protection Bureau (CFPB), and all 50 U.S. states and territories sued over the company’s failure to take “reasonable steps” to secure its sensitive personal data.
Privacy and data protection are enforced by a growing number of regulations around the world and people are actively demanding privacy protection — and legislators are reacting. More than 60 countries have introduced privacy laws in response to citizens’ cry for transparency and control. By 2023, 65% of the world’s population will have its personal information covered under modern privacy regulations, up from 10% today, according to Gartner. There is a convergence of data privacy principles, standards and regulations on a common set of fundamental principles.
The opportunities to use data are growing exponentially, but so too are the business and financial risks as the number of data protection and privacy regulations grows internationally.
Join this webinar to learn more about:
- Trends in modern privacy regulations
- The impact on organizations to protect and use sensitive data
- Data privacy principles
- The impact of General Data Protection Regulation (GDPR) and data transfer between US and EU
- The evolving CCPA, the new PCI DSS version 4 and new international data privacy laws or regulations
- Data privacy best practices, use cases and how to control sensitive personal data throughout the data life cycle
What is tokenization in blockchain - BCS London
BCS North London Branch in association with Central London Branch webinar (by GoToWebinar) Date: 2nd December 2020 Time: 18.00 to 19.30 Event title: Blockchain tokenization “What is tokenization in Blockchain?”
Agenda
Blockchain
What is Blockchain?
Use cases, trends and risks
Vendors and platforms
Data protection techniques and scalability
Tokenization
Digital business
Convert a digital value into a digital token
Local and central models
Cloud
Tokenization in Hybrid cloud
Protecting data privacy in analytics and machine learning - ISACA
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.
Nov 2 security for blockchain and analytics ulf mattsson 2020 nov 2b
Blockchain
- What is Blockchain?
- Blockchain trends
Emerging data protection techniques
- Secure multiparty computation
- Trusted execution environments
- Use cases for analytics
- Industry Standards
Tokenization
- Convert a digital value into a digital token
- Tokenization local or in a centralized model
- Tokenization and scalability
Cloud
- Analytics in Hybrid cloud
Unlock the potential of data security 2020
Explore challenges of managing and protecting data. We'll share best practices on establishing the right balance between privacy, security, and compliance
More from Ulf Mattsson (20)
Jun 29 new privacy technologies for unicode and international data standards ...
Jun 29 new privacy technologies for unicode and international data standards ...
Jun 15 privacy in the cloud at financial institutions at the object managemen...
Jun 15 privacy in the cloud at financial institutions at the object managemen...
May 6 evolving international privacy regulations and cross border data tran...
May 6 evolving international privacy regulations and cross border data tran...
Secure analytics and machine learning in cloud use cases
Secure analytics and machine learning in cloud use cases
Evolving international privacy regulations and cross border data transfer - g...
Evolving international privacy regulations and cross border data transfer - g...
Data encryption and tokenization for international unicode
Data encryption and tokenization for international unicode
GDPR and evolving international privacy regulations
GDPR and evolving international privacy regulations
Privacy preserving computing and secure multi-party computation ISACA Atlanta
Privacy preserving computing and secure multi-party computation ISACA Atlanta
Safeguarding customer and financial data in analytics and machine learning
Safeguarding customer and financial data in analytics and machine learning
Protecting data privacy in analytics and machine learning ISACA London UK
Protecting data privacy in analytics and machine learning ISACA London UK
New opportunities and business risks with evolving privacy regulations
New opportunities and business risks with evolving privacy regulations
Protecting data privacy in analytics and machine learning - ISACA
Protecting data privacy in analytics and machine learning - ISACA
Nov 2 security for blockchain and analytics ulf mattsson 2020 nov 2b
Nov 2 security for blockchain and analytics ulf mattsson 2020 nov 2b
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Bio cv ulf mattsson
- 1. 1 Ulf Mattsson, BIO + Mr. Mattsson is currently the Head of Innovation at TokenEx, a cloud-based data security company, was previously Chief Technology Officer at Atlantic BT Security Solutions, and earlier Chief Technology Officer at Compliance Engineering. He was the Chief Technology Officer and a technology founder of Protegrity. + Prior to Protegrity, he worked 20 years at IBM's Research and Development organization, in the areas of Application and Database Security. He also worked at companies providing Data Discovery Services, Cloud Application Security Brokers, Web Application Firewalls, Managed Security Service, Security Operation Center, and Cybersecurity consulting. + Mr. Mattsson is an Inventor of 73 Awarded and Issued US Patents. + He delivered joint Application and Data Protection products and development teams at IBM, Microsoft, Hewlett-Packard, Oracle, Teradata, and RSA Security (Dell). Mr. Mattsson is a also advising companies in the area of AI, Machine Learning and Quantum Computing technologies. + Mr. Mattsson also owns and manages the BrightTALK “Cybersecurity - The No Spin Zone” and “The Blockchain Channel.”