Computing arbitrary functions of encrypted data

By Craig Gentry. 2010

693 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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In Computational Theory and Mathematics

Citations in the corpus

Robust Aggregation for Federated Learning (2019, background): “Frameworks to guarantee privacy of user data include differential privacy [31, 45] and homomorphic encryption [35].”
Single-Database Private Information Retrieval from Fully Homomorphic Encryption (2013, background): “In 2009, Gentry [15], [16], [17], [18] constructed the first fully homomorphic encryption (FHE) scheme using latticebased cryptography.”
6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication (2019, background): “For example, a mobile user wishing to run a remote machine-learning algorithm on its own data could use homomorphic encryption [16].”
Faster Fully Homomorphic Encryption (2010, background): “The article [12] provides an intuitive description of Gentry's fully homomorphic scheme.”
Generic Attacks on Secure Outsourced Databases (2016, background): “…techniques exist that can be applied to this problem, ranging from solutions based on tools such as fully homomorphic encryption (FHE) [20, 46] or oblivious RAM (ORAM) [23, 25] to more practical solutions based on weaker primitives such as structural encryption [33] or deterministic…”
Is privacy privacy? (2018, methodology): “In fact, modern cryptographic methods such as secure multiparty computation [30] and homomorphic encryption [31] extend the notion of semantic security to settings in which several parties (each holding a private input) compute a”
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020, background): “Since its discovery, many algorithmic improvements have been made to improve performance [6], [8], [9], [22], [25]–[27].”
Secure and practical outsourcing of linear programming in cloud computing (2011, background): “However, ordinary data encryption techniques in essence prevent cloud from performing any meaningful operation of the underlying plaintext data [3], making the computation over encrypted data a very hard problem.”
Privacy-Preserving Data Processing with Flexible Access Control (2020, methodology): “To realize arbitrary computations over ciphertexts, schemes based on fully homomorphic encryption (FHE) were designed [10], [11], [12].”
A Survey of Computation Offloading for Mobile Systems (2012, background): “Solutions include steganography [47], homomorphic encryption [21, 22], hardware-based secure execution [7].”
Privacy-Preserving Machine Learning for Speech Processing (2012, methodology): “Although the construction satisfies the necessary properties for FHE, it is found to be computationally inefficient to be used in practice [Gentry, 2010b] and developing practical FHE schemes is an active area of research [Lauter et al., 2011].”
Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, background): “r [36]. In this respect, prominent privacypreserving techniques in ML include data anonymisation [37], differential privacy [38], secure multi-party computation (SMC) [39], and homomorphic encryption [40]. 1) Data Anonymisation Data anonymisation or de-identiﬁcation is a technique to hide (e.g., has”
Encrypted Accelerated Least Squares Regression (2017, methodology): “Copyright 2017 by the author(s). volves adapting existing methodology for regression analysis in such a way as to enable full computation within the mathematical and computational constraints of recently developed fully homomorphic encryption schemes (Gentry, 2010; Fan and Vercauteren, 2012).”