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SC
Dr. Sarah Chen
Stanford · NLP & interpretability
91
fit
Fairness + NLP projects overlap with her recent work.
Strong methods fit: model evaluation and interpretability.
Clear outreach angle for current trustworthy AI research.
Outreach angle
Ask how her lab is extending interpretability methods into fairness audits for real-world AI systems.
Yevgeniy Dodis is a Professor at the Department of Computer Science at the Courant Institute of Mathematical Sciences, New York University, and an IACR Fellow. His main research interests are in Cryptography, with particular focus on Leakage-Resilient Cryptography, Random Number Generation, Cryptography with Biometrics and Other Noisy Data, Hash Functions and the Random Oracle Model, Information-Theoretic Cryptography, and Secure (Group) Messaging. He is also interested in broader areas of Theoretical Computer Science, including Algorithms, Complexity, and Combinatorics.
Professor Dodis has made significant contributions to cryptology, especially in the areas of cryptographic randomness and symmetric-key primitives. His work has been recognized with awards such as the IACR Test of Time Award in 2019 and 2021, and he was named an IACR Fellow in 2020 for his fundamental contributions. He has been actively involved in the cryptography community through organizing seminars, conferences, and serving as an editor for the Journal of Cryptology. His academic career includes advising numerous PhD students and postdoctoral researchers, and he has participated in many prominent conferences and workshops, often in leadership roles such as program co-chair and general chair.
Modern messengers use advanced end-to-end encryption protocols to protect message content even if user secrets are ever temporarily exposed. Yet, encryption alone does not prevent user tracking, as protocols often attach metadata, such as sequence numbers, public keys, or even plain user identifiers. This metadata reveals the social network as well as communication patterns between users. Existing protocols that hide metadata in Signal (i.e., Sealed Sender), for MLS-like constructions (Hashimoto et al., CCS 2022), or in mesh networks (Bienstock et al., CCS 2023) are relatively inefficient or specially tailored for only particular settings. Moreover, all existing practical solutions reveal crucial metadata upon exposures of user secrets.
Everlasting (EL) privacy offers an attractive solution to the Store-Now-Decrypt-Later (SNDL) problem, where future increases in the attacker's capability could break systems which are believed to be secure today. Instead of requiring full information-theoretic security, everlasting privacy allows computationally-secure transmissions of ephemeral secrets, which are only "effective" for a limited periods of time, after which their compromise is provably useless for the SNDL attacker. In this work we revisit such everlasting privacy model of Dodis and Yeo (ITC'21), which we call Hypervisor EverLasting Privacy (HELP). HELP is a novel architecture for generating shared randomness using a network of semi-trusted servers (or "hypervisors"), trading the need to store/distribute large shared secrets with the assumptions that it is hard to: (a) simultaneously compromise too many publicly accessible ad-hoc servers; and (b) break a computationally-secure encryption scheme very quickly. While Dodis and Yeo presented good HELP solutions in the asymptotic sense, their solutions were concretely expensive and used heavy tools (like large finite fields or gigantic Toeplitz matrices). We abstract and generalize the HELP architecture to allow for more efficient instantiations, and construct several concretely efficient HELP solutions. Our solutions use elementary cryptographic operations, such as hashing and message authentication. We also prove a very strong composition theorem showing that our EL architecture can use any message transmission method which is computationally-secure in the Universal Composability (UC) framework. This is the first positive composition result for everlasting privacy, which was otherwise known to suffer from many "non-composition" results (Müller-Quade and Unruh; J of Cryptology'10).
