Pseudonymisation

Résumé

La pseudonymisation est un traitement de données à caractère personnel de manière qu'on ne puisse pas attribuer les données à une personne physique sans avoir recours à des informations supplémentaires. Le processus de pseudonymisation est moins fort que le processus d'anonymisation qui vise à empêcher totalement et de manière irréversible la possibilité d'identifier une personne. En France, à la suite de la loi pour une République numérique du , le rapport Cadiet préconise de pseudonymiser les décisions de justice pour les diffuser en open data. Notes et références Articles connexes

Reconnaissance d'entités nommées

Catégorie:Données personnelles Catégorie:Identification personnelle

Source officielle

https://fr.wikipedia.org/wiki/Pseudonymisation

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Adjustable deterministic pseudonymization of speech

Subrahmanya Pavankumar Dubagunta, Mathew Magimai Doss

While public speech resources become increasingly available, there is a growing interest to preserve the privacy of the speakers, through methods that anonymize the speaker information from speech while preserving the spoken linguistic content. In this paper, a method for pseudonymization (reversible anonymization) of speech is presented, that allows to obfuscate the speaker identity in untranscribed running speech. The approach manipulates the spectrotemporal structure of the speech to simulate a different length and structure of the vocal tract by modifying the formant locations, as well as by altering the pitch and speaking rate. The method is deterministic and partially reversible, and the changes are adjustable on a continuous scale. The method has been evaluated in terms of (i) ABX listening experiments, and (ii) automatic speaker verification and speech recognition. ABX experimental results indicate that the speaker identifiability among forced choice pairs reduced from over 90% to less than 70% through pseudonymization, and that de-pseudonymization was partially effective. An evaluation on the VoicePrivacy 2020 challenge data showed that the proposed approach performs better than the signal processing based baseline method that uses McAdams coefficient and performs slightly worse than the neural source filtering based baseline method. Further analysis showed that the proposed approach: (i) is comparable to the neural source filtering baseline based method in terms of phone posterior feature based objective intelligibility measure, (ii) preserves formant tracks better than the McAdams based method, and (iii) preserves paralinguistic aspects such as dysarthria in several speakers.

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD2022

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Hang With Your Buddies to Resist Intersection Attacks

Bryan Alexander Ford

Some anonymity schemes, such as DC-nets and MIX cascades, can guarantee anonymity even against traffic analysis – provided messages are independent and unlinkable. Users in practice often desire pseudonymity – sending messages intentionally linkable to each other but not to the sender – but pseudonymity in dynamic networks exposes users to intersection attacks. We present Buddies, the first systematic attempt to offer intersection attack resistant pseudonyms in practical anonymity systems. Buddies groups users dynamically into buddy sets, controlling message transmission to make buddies within a set behaviorally indistinguishable to a traffic-monitoring adversary. Intersection attack resistance does not come “for free,” of course, and Buddies offers users control over the inevitable tradeoffs between anonymity, latency, and the useful lifetime of a pseudonym. Using trace-based simulations and a working prototype, we find that Buddies can guarantee non-trivial anonymity set sizes in realistic chat/microblogging scenarios, for both short-lived and long-lived pseudonyms.

2013

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Enforcing Privacy for Teenagers in Online Inquiry Learning Spaces

Denis Gillet, Sten Govaerts, Adrian Christian Holzer, Na Li

In the Go-Lab European project, we are investigating an online lab portal that enables students to perform personalized scientific experiments with online labs. To protect sensitive student data, such as user activity data and learning preferences, this paper presents a classroom-like pseudonymity approach that fulfills the specific requirements of the Go-Lab portal: (1) enforcing student privacy, (2) enabling personalization, and (3) providing a teenager-friendly design.

2014

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