Results 261 to 270 of about 27,257 (306)
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SSRN Electronic Journal, 2023
A signal is privacy‐preserving with respect to a collection of privacy sets if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy‐preserving signals for arbitrary state space and arbitrary privacy sets.
Strack, Philipp, Yang, Kai Hao
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A signal is privacy‐preserving with respect to a collection of privacy sets if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy‐preserving signals for arbitrary state space and arbitrary privacy sets.
Strack, Philipp, Yang, Kai Hao
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Data Mining and Knowledge Discovery, 2007
We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms.
Gambs, Sebastien +2 more
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We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms.
Gambs, Sebastien +2 more
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Proceedings of the 9th Symposium on Identity and Trust on the Internet, 2010
This paper describes and contrasts two families of schemes that enable a user to purchase digital content without revealing to anyone what item he has purchased. One of the basic schemes is based on anonymous cash, and the other on blind decryption.
Radia J. Perlman +2 more
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This paper describes and contrasts two families of schemes that enable a user to purchase digital content without revealing to anyone what item he has purchased. One of the basic schemes is based on anonymous cash, and the other on blind decryption.
Radia J. Perlman +2 more
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2008 IEEE 24th International Conference on Data Engineering, 2008
In this paper, we design a system for mutually distrustful entities to perform privacy preserving joins, leveraging the power of a memory-limited secure coprocessor. Under this setting, we critique a questionable assumption in a previous privacy definition [1] that leads to unnecessary information leakage.
Yaping Li, Minghua Chen 0001
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In this paper, we design a system for mutually distrustful entities to perform privacy preserving joins, leveraging the power of a memory-limited secure coprocessor. Under this setting, we critique a questionable assumption in a previous privacy definition [1] that leads to unnecessary information leakage.
Yaping Li, Minghua Chen 0001
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2023
Der Privatsphärenschutz von persönlichen Informationen gegenüber Dienstbetreibern ist neben dem Absichern von modernen Client-Server-Anwendungen immer noch eine große Herausforderung für Softwareentwickler. Das Ableiten von Nutzerinnen-Informationen durch direkte und indirekte Datenanalysen ist weit verbreitet.
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Der Privatsphärenschutz von persönlichen Informationen gegenüber Dienstbetreibern ist neben dem Absichern von modernen Client-Server-Anwendungen immer noch eine große Herausforderung für Softwareentwickler. Das Ableiten von Nutzerinnen-Informationen durch direkte und indirekte Datenanalysen ist weit verbreitet.
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Proceedings of the 2nd Asia-Pacific Workshop on Networking, 2018
Autonomous systems (ASes) on the Internet increasingly rely on Internet Exchange Points (IXPs) for peering. A single IXP may interconnect several 100s or 1000s of participants (ASes) all of which might peer with each other through BGP sessions. IXPs have addressed this scaling challenge through the use of route servers.
Xiaohe Hu +4 more
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Autonomous systems (ASes) on the Internet increasingly rely on Internet Exchange Points (IXPs) for peering. A single IXP may interconnect several 100s or 1000s of participants (ASes) all of which might peer with each other through BGP sessions. IXPs have addressed this scaling challenge through the use of route servers.
Xiaohe Hu +4 more
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Proceedings of the 2005 ACM SIGMOD international conference on Management of data, 2005
We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (randomized) in order to preserve privacy before it is integrated at the server. We develop formal notions of privacy obtained from data perturbation and show that our perturbation
Rakesh Agrawal 0001 +2 more
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We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (randomized) in order to preserve privacy before it is integrated at the server. We develop formal notions of privacy obtained from data perturbation and show that our perturbation
Rakesh Agrawal 0001 +2 more
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Communications of the ACM, 2020
Can you answer a poll without revealing your true preferences and have the results of the poll still be accurate?
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Can you answer a poll without revealing your true preferences and have the results of the poll still be accurate?
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Privacy-Preserving Smart Metering
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society, 2011Smart grid proposals threaten user privacy by potentially disclosing fine-grained consumption data to utility providers, primarily for time-of-use billing, but also for profiling, settlement, forecasting, tariff and energy efficiency advice. We propose a privacy-preserving protocol for general calculations on fine-grained meter readings, while keeping ...
Alfredo Rial, George Danezis
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Privacy-preserving deep learning
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into the foundation of new AI-based services on the Internet.
Reza Shokri, Vitaly Shmatikov
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