Arrow Research search

Author name cluster

David Pointcheval

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

I&C Journal 2023 Journal Article

Traceable constant-size multi-authority credentials

  • Chloé Hébant
  • David Pointcheval

Many attribute-based anonymous credential (ABC) schemes have been proposed, enabling users to anonymously prove possession of attributes. Recent papers introduced constant-size credentials issued by a single credential issuer for showing a subset of attributes. However, proving possession of attributes from multiple issuers typically requires independent credentials. Only aggregate signature-based attribute-based credential schemes can overcome this limitation. This paper presents new ABC schemes using aggregate signatures with randomizable tags. The schemes consider malicious credential issuers, adaptive corruptions, and collusions with malicious users. While our constructions only support selective attribute disclosures to remain compact, our approach significantly improves the complexity in time and memory for showing multiple attributes. For the first time, the cost for the prover becomes (almost) independent of the number of attributes and issuers. Whereas anonymous credentials require privacy of the user, we also propose the first schemes allowing traceability by a specific tracing authority.

Details DOI

NeurIPS Conference 2019 Conference Paper

Partially Encrypted Deep Learning using Functional Encryption

  • Théo Ryffel
  • David Pointcheval
  • Francis Bach
  • Edouard Dufour-Sans
  • Romain Gay

Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the calculation: it provides a decryption key which allows one to learn a specific function evaluation of some encrypted data. We then show how to use it in machine learning to partially encrypt neural networks with quadratic activation functions at evaluation time and we provide a thorough analysis of the information leaks based on indistinguishability of data items of the same label. Last, since several encryption schemes cannot deal with the last thresholding operation used for classification, we propose a training method to prevent selected sensitive features from leaking which adversarially optimizes the network against an adversary trying to identify these features. This is of great interest for several existing works using partially encrypted machine learning as it comes with almost no cost on the model's accuracy and significantly improves data privacy.

PDF Details
v2026.09.13