International audienceCrowdsourcing platforms dedicated to work are used by a growing number of individuals and organizations, for tasks that are more and more diverse, complex, and that require very specific skills. These highly detailed worker profiles enable high-quality task assignments but may disclose a large amount of personal information to the central platform (e.g., personal preferences, availabilities, wealth, occupations), jeopardizing the privacy of workers. In this paper, we propose a lightweight approach to protect workers privacy against the platform along the current crowdsourcing task assignment process. Our approach (1) satisfies differential privacy by letting each worker perturb locally her profile before sending it to the platform, and (2) copes with the resulting perturbation by leveraging a taxonomy defined on workers profiles. We overview this approach below, explaining the lightweight upgrades to be brought to the participants. We have also shown (full version of this paper [1]) formally that our approach satisfies differential privacy, and empirically, through experiments performed on various synthetic datasets, that it is a promising research track for coping with realistic cost and quality requirements

The open data movement is leading to the massive publishing of court records online, increasing the transparency and accessibility of justice, and enabling the advent of legal technologies building on the wealth of legal data available. However, the sensitive nature of legal decisions also raises important privacy issues. Most of the current practices address the resulting privacy / transparency trade-off by combining access control with (manual or semi-manual) text redaction. In this work, we argue that current practices are insufficient for coping with the massive access to legal data, in the sense that restrictive access control policies are detrimental to both openness and to utility while text redaction is unable to provide sound privacy protection. Thus, we advocate for a integrative approach that could benefit from the latest developments in the privacy-preserving data publishing domain. We present a detailed analysis of the problem and of the current approaches, and propose a straw man multimodal architecture paving the way to a full-fledged privacypreserving legal data publishing system.