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The open data movement is leading to the massive publishing of court records online, increasing transparency and accessibility of justice, and to the design of legal technologies building on the wealth of legal data available. However, the sensitive nature of legal decisions also raises important privacy issues. Current practices solve the resulting privacy versus transparency trade-off by combining access control with (manual or semi-manual) text redaction. In this work, we claim that current practices are insufficient for coping with massive access to legal data (restrictive access control policies is detrimental to openness and to utility while text redaction is unable to provide sound privacy protection) and advocate for a in-tegrative approach that could benefit from the latest developments of the privacy-preserving data publishing domain. We present a thorough analysis of the problem and of the current approaches, and propose a straw man multimodal architecture paving the way to a full-fledged privacy-preserving legal data publishing system.
Many socially valuable activities depend on sensitive information, such as medical research, public health policies, political coordination, and personalized digital services. This is often posed as an inherent privacy trade-off: we can benefit from
The rapid growth in digital data forms the basis for a wide range of new services and research, e.g, large-scale medical studies. At the same time, increasingly restrictive privacy concerns and laws are leading to significant overhead in arranging fo
Since the global spread of Covid-19 began to overwhelm the attempts of governments to conduct manual contact-tracing, there has been much interest in using the power of mobile phones to automate the contact-tracing process through the development of
The purpose of Secure Multi-Party Computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output
This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerat