Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

We show how multiple data-owning parties can collabora tively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a...

Coping with the general data protection regulation; anonymization through multi-party computation technology

Analysing combined data sets can result in signifi cant added value for many organisations, but the GDPR has put strict constraints on processing personal data. Anonymization by using Multi-Party Computation (MPC) however may off er organizations some relief of the perceived burden of GDPR under specifi c conditions. In this paper, we will...

SoK: Engineering privacy-aware high-tech systems

The processing of personal data is becoming a key business factor, especially for high-tech system industries such as automotive and healthcare service providers. To protect such data, the European Union (EU) has introduced the General Data Protection Regulation (GDPR), with the aim to standardize and strengthen data protection policies across...

Digital enforceable contracts (DEC) Making smart contracts smarter

The combination of smart contracts with blockchain technology enables the authentication of the contract and limits the risks of non-compliance. In principle, smart contracts can be processed more efficiently compared to traditional paper-based contracts. However, current smart contracts have very limited capabilities with respect to normative...

EFLINT A domain-specific language for executable norm specifications

Privacy and user trust in context-aware systems

Context-aware systems (CAS) that collect personal information are a general trend. This leads to several privacy considerations, which we outline in this paper. We present as use-case the SWELL system, which collects information from various contextual sensors to provide support for well-being at work. We address privacy from two perspectives: 1...

Open-source intelligence and privacy by design

As demonstrated by other papers on this issue, open-source intelligence (OSINT) by state authorities poses challenges for privacy protection and intellectual-property enforcement. A possible strategy to address these challenges is to adapt the design of OSINT tools to embed normative requirements, in particular legal requirements. The experience...

Privacy by Design: An alternative to existing practice in safeguarding privacy

Purpose: The purpose of this paper is to develop/elaborate the concept Privacy by Design (PbD) and to explore the validity of the PbD framework. Design/methodology/approach: Attention for alternative concepts, such as PbD, which might offer surplus value in safeguarding privacy, is growing. Using PbD to design for privacy in ICT systems is still...