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Há 5 dias · Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. Theory of Cryptography Vol. 3876, 265–284.
Há 5 dias · Cynthia Dwork and Guy N Rothblum. Concentrated differential privacy. arXiv preprint arXiv:1603.01887, 2016. Google Scholar; Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference, pages 265-284. Springer, 2006. Google Scholar Digital Library
Há 6 dias · Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. Our data, ourselves: Privacy via distributed noise generation. In Advances in Cryptology - EUROCRYPT 2006, pages 486-503, 2006.
21 de mai. de 2024 · ABSTRACT. With the increasing adoption of decentralized information systems based on a variety of permissionless blockchain networks, the choice of consensus mechanism is at the core of many controversial discussions. Ethereum's recent transition from proof-of-work (PoW) to proof-of-stake (PoS)-based consensus has further fueled the ...
24 de mai. de 2024 · Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard, Affiliated Faculty at Harvard Law School and Department of Statistics, and Distinguished Scientist at Microsoft, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation.
Há 4 dias · Cynthia Dwork: Advances in Cryptology - CRYPTO 2006, 26th Annual International Cryptology Conference, Santa Barbara, California, USA, August 20-24, 2006, Proceedings. Lecture Notes in Computer Science 4117, Springer 2006 , ISBN 3-540-37432-9 [contents]
28 de mai. de 2024 · To alleviate these issues, Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith introduced the definition of differential privacy (DP) in 2006. Roughly speaking, an algorithm is DP if its distribution over outputs is insensitive to adding or removing any data point from its input.
