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Linear queries, as the basis of broad analysis tasks, are often released through privacy mechanisms based on differential privacy (DP), the most popular framework for privacy protection. However, DP adopts a context-free definition that…

Information Theory · Computer Science 2026-04-14 Heng Zhao , Sara Saeidian , Tobias J. Oechtering

Dynamic searchable symmetric encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion named forward and backward privacy is expected for newly…

Cryptography and Security · Computer Science 2023-09-14 Lei Xu , Leqian Zheng , Chengzhi Xu , Xingliang Yuan , Cong Wang

In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials…

Computer Vision and Pattern Recognition · Computer Science 2023-12-13 Renyang Liu , Wei Zhou , Xin Jin , Song Gao , Yuanyu Wang , Ruxin Wang

Machine Learning models, extensively used for various multimedia applications, are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox models are commercially valuable to adversaries, making them…

Cryptography and Security · Computer Science 2020-02-04 Vasisht Duddu , D. Vijay Rao

Quantum key distribution (QKD offers a long-term solution to establish information-theoretically secure keys between two distant users. In practice, with a careful characterization of quantum sources and the decoy-state method,…

Large Language Models (LLMs) are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications, where privacy is paramount. This paper introduces Whisper Leak, a side-channel attack that…

Cryptography and Security · Computer Science 2025-11-06 Geoff McDonald , Jonathan Bar Or

Recent Searchable Symmetric Encryption (SSE) schemes enable secure searching over an encrypted database stored in a server while limiting the information leaked to the server. These schemes focus on hiding the access pattern, which refers…

Cryptography and Security · Computer Science 2020-10-08 Simon Oya , Florian Kerschbaum

In contrast to classical public-key cryptosystems, where the security of encoded messages relies on on computational assumptions, Quantum Key Distribution (QKD) enables two distant parties to establish a shared secret key that, when…

Quantum key distribution (QKD) security fundamentally relies on the ability to distinguish genuine quantum correlations from classical eavesdropper simulations, yet existing certification methods lack rigorous statistical guarantees under…

Quantum Physics · Physics 2025-12-04 Davut Emre Tasar , Ceren Ocal Tasar

Measuring the information leakage is critical for evaluating the practical security of cryptographic devices against side-channel analysis. Information-theoretic measures can be used (along with Fano's inequality) to derive upper bounds on…

Information Theory · Computer Science 2022-05-17 Wei Cheng , Yi Liu , Sylvain Guilley , Olivier Rioul

Searchable symmetric encryption schemes often unintentionally disclose certain sensitive information, such as access, volume, and search patterns. Attackers can exploit such leakages and other available knowledge related to the user's…

Cryptography and Security · Computer Science 2024-03-05 Hao Nie , Wei Wang , Peng Xu , Xianglong Zhang , Laurence T. Yang , Kaitai Liang

Quantum key distribution (QKD) provides information theoretic security based on quantum mechanics, however, its practical deployment is challenged by imperfections of source devices. Among various source loopholes, correlations between…

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In…

Cryptography and Security · Computer Science 2022-09-20 Valentin Hartmann , Léo Meynent , Maxime Peyrard , Dimitrios Dimitriadis , Shruti Tople , Robert West

Large Language Models (LLMs) have shown remarkable progress in multiple-choice question answering (MCQA), but their inherent unreliability, such as hallucination and overconfidence, limits their application in high-risk domains. To address…

Computation and Language · Computer Science 2025-08-08 Guang Yang , Xinyang Liu

Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction. Yet their susceptibility to privacy leakage via Membership Inference Attacks (MIA) remains…

Machine Learning · Computer Science 2026-02-10 Yuetian Chen , Kaiyuan Zhang , Yuntao Du , Edoardo Stoppa , Charles Fleming , Ashish Kundu , Bruno Ribeiro , Ninghui Li

With the emergence of an information society, the idea of protecting sensitive data is steadily gaining importance. Conventional encryption methods may not be sufficient to guarantee data protection in the future. Quantum key distribution…

Quantum Physics · Physics 2016-09-20 Nitin Jain , Birgit Stiller , Imran Khan , Dominique Elser , Christoph Marquardt , Gerd Leuchs

For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while…

Machine Learning · Computer Science 2020-08-24 Praneeth Vepakomma , Abhishek Singh , Otkrist Gupta , Ramesh Raskar

Quantum Key Distribution (QKD) is a promising technology for secure communication. Nevertheless, QKD is still treated with caution in certain contexts due to potential gaps between theoretical models and actual QKD implementations. A common…

Distributed-phase-reference quantum key distribution stands out for its easy implementation with present day technology. Since many years, a full security proof of these schemes in a realistic setting has been elusive. For the first time,…

Quantum Physics · Physics 2013-07-05 Tobias Moroder , Marcos Curty , Charles Ci Wen Lim , Le Phuc Thinh , Hugo Zbinden , Nicolas Gisin

Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data. However, we argue that it is possible to have reconstructed data identically distributed as the original…

Machine Learning · Statistics 2026-05-15 Xinwei Shen , Nicolai Meinshausen