Description
Imagine the government is taking a census, and you as an individual are worried that by participating, private information about you (such as your address, age, ethnicity, etc.) may eventually be revealed when the government publishes the census data. How can the government assure you that by using an appropriate release mechanism that "sanitizes" census data, no individual's privacy will be compromised?<br/> This question has been studied for a long time in the statistics community, and more recently the computer science community has contributed the formal notion of differential privacy, which captures the idea that "no individual's data can have a large effect on the output of the release mechanism". This has been interpreted to mean that individuals should be comfortable revealing their information, since little private information is leaked. In this talk, we first give an introduction to this fast-developing area of research. We then investigate the above interpretation about the guarantees of differential privacy. We argue that the interpretation is incomplete because unless participation in the database somehow explicitly benefits the individuals, they will always refuse to participate regardless of whether the release mechanism is differentially private or not. We then show that by combining differential privacy with the notion of incentives and truthfulness from game theory, one can take (almost) any release mechanism that motivates individuals to participate and modify it so that in addition it satisfies differential privacy.
Next sessions
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Key Attack on the ACDGV Matrix Encryption Scheme
Speaker : Anmoal Porwal - Technical University of Munich
I will present our key-recovery attack on the ACDGV public-key encryption scheme proposed at ASIACRYPT 2024 by Aragon, Couvreur, Dyseryn, Gaborit, and Vinçotte. The secret key is a Gabidulin code hidden by appending random rows and columns and by left- and right-multiplication with invertible matrices. Our attack exploits the resulting algebraic structure to recover an equivalent secret key. It[…]-
Cryptography
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Asymmetric primitive
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Module Learning With Errors and Structured Extrapolated Dihedral Cosets
Speaker : Jinwei Zheng - Télécom Paris
The Module Learning With Errors (MLWE) problem is the fundamental hardness assumption underlying the key encapsulation and signature schemes ML-KEM and ML-DSA, which have been selected by NIST for post-quantum cryptography standardization. Understanding its quantum hardness is crucial for assessing the security of these standardized schemes. Inspired by the equivalence between LWE and[…]-
Cryptography
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