Results 1 to 10 of about 40,084 (262)
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Econometrics with Privacy Preservation
Operations Research, 2019Summary: Many data are sensitive in areas such as finance, economics, and other social sciences. We propose an ER (encryption and recovery) algorithm that allows a central administration to do statistical inference based on the encrypted data, while still preserving each party's privacy even for a colluding majority in the presence of cyber attack.
Ning Cai 0003, Steven Kou
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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 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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Privacy-Preserving Trust Negotiations
2005Trust negotiation is a promising approach for establishing trust in open systems, where sensitive interactions may often occur between entities with no prior knowledge of each other. Although several proposals today exist of systems for the management of trust negotiations none of them addresses in a comprehensive way the problem of privacy ...
ELISA BERTINO +2 more
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Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019
The goals of learning from user data and preserving user privacy are often considered to be in conflict. This presentation will demonstrate that there are contexts when provable privacy guarantees can be an enabler for better web search and data mining (WSDM), and can empower researchers hoping to change the world by mining sensitive user data.
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The goals of learning from user data and preserving user privacy are often considered to be in conflict. This presentation will demonstrate that there are contexts when provable privacy guarantees can be an enabler for better web search and data mining (WSDM), and can empower researchers hoping to change the world by mining sensitive user data.
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