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Un pseudonyme est un nom d'emprunt adopté par une ou plusieurs personnes pour exercer une activité sous un autre nom que celui de son identité officielle. Il se distingue du surnom en ceci qu'il est choisi par la personne qui le porte au lieu de lui être attribué par un tiers. Son usage est fréquent dans certains milieux, tels que le milieu artistique : auteurs, acteurs, etc., ou scientifique : groupe d’auteurs (Bourbaki), confidentialité (Student), etc. L'usage du pseudonyme peut avoir plusieurs motivations : substitution à un nom jugé imprononçable, trop marqué ethniquement ou « peu glamour », protection de l'identité réelle, motivation artistique, même patronyme dans le même domaine (Stewart Granger est un pseudonyme imposé, car ses vrais prénom-nom étaient... James Stewart), etc. Un mode assez courant de formation de pseudonymes est d'utiliser une anagramme de son nom réel (certaines œuvres de François Rabelais sont parues sous le pseudonyme d’Alcofribas Nasier). Dans le

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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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MorphIT: Morphing Packet Reports for Internet Transparency

Bryan Alexander Ford, Georgia Fragkouli

Can we improve Internet transparency without worsening user anonymity? For a long time, researchers have been proposing transparency systems, where traffic reports produced at strategic network points help assess network behavior and verify service-level agreements or neutrality compliance. However, such reports necessarily reveal when certain traffic appeared at a certain network point, and this information could, in principle, be used to compromise low-latency anonymity networks like Tor. In this paper, we examine whether more Internet transparency necessarily means less anonymity. We start from the information that a basic transparency solution would publish about a network and study how that would impact the anonymity of the network’s users. Then we study how to change, in real time, the time granularity of traffic reports in order to preserve both user anonymity and report utility. We evaluate with real and synthetic data and show that our algorithm can offer a good anonymity/utility balance, even in adversarial scenarios where aggregates consist of very few flows.

Sciendo2019

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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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