Tristan Allard

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An increasing amount of personal data is automatically gathered and stored on servers by administrations, hospitals, insurance companies, etc. Citizen themselves often count on internet companies to store their data and make them reliable and highly available through the internet. However, these benefits must be weighed against privacy risks incurred by(More)
We present the WebContent platform for managing distributed repositories of XML and semantic Web data. The platform allows integrating various data processing building blocks (crawling, translation, semantic annotation, full-text search, structured XML querying, and semantic querying), presented as Web services, into a large-scale efficient platform. Calls(More)
The advent of on-body/at-home sensors connected to personal devices leads to the generation of fine grain highly sensitive personal data at an unprecendent rate. However, despite the promises of large scale analytics there are obvious privacy concerns that prevent individuals to share their personnal data. In this paper, we propose <i>Chiaroscuro</i>, a(More)
An increasing number of surveys and articles highlight the failure of database servers to keep confidential data really private. Even without considering their vulnerability against external or internal attacks, mere negligences often lead to privacy disasters. The advent of powerful smart portable tokens, combining the security of smart card(More)
While most of the work done in Privacy-Preserving Data Publishing does the assumption of a trusted central publisher, this paper advocates a fully decentralized way of publishing anonymized datasets. It capitalizes on the emergence of more and more powerful and versatile Secure Portable Tokens raising new alternatives to manage and protect personal data.(More)
The goal of Privacy-Preserving Data Publishing (PPDP) is to generate a sanitized (i.e. harmless) view of sensitive personal data (e.g. a health survey), to be released to some agencies or simply the public. However, traditional PPDP practices all make the assumption that the process is run on a trusted central server. In this article, we argue that the(More)
This article addresses the issue of adapting the traditional model of Privacy-Preserving Data Publishing (PPDP) to an environment composed of a large number of tamper-resistant Secure Portable Tokens (SPTs) containing private personal data. Our model assumes that the SPTs seldom connect to a highly available but untrusted infrastructure. We illustrate the(More)