Add to ORKGWe present a mathematical formulation for the optimization of query forgery for private information retrieval, in the sense that the privacy risk is minimized for a given traffic and processing overhead. The privacy risk is measured as an information- theoretic divergence between the user’s query distribution and the population’s, which includes the entropy of the user’s distribution as a special case. We carefully justify and interpret our privacy criterion from diverse perspectives. Our formulation poses a mathematically tractable problem that bears substantial resemblance with rate-distortion theory
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We consider the problem of minimizing the communication in single-database private information retri...
Private Information Retrieval (PIR), despite being well studied, is computationally costly and arduo...
Abstract. In previous work, we presented a novel information-theoretic privacy criterion for query f...
Recommendation systems are information-filtering systems that tailor information to users on the bas...
Recommendation systems are information-filtering systems that tailor information to users on the bas...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Abstract—We address the problem of query profile obfuscation by means of partial query exchanges bet...
Abstract. The notion of differential privacy has emerged in the area of statisti-cal databases to pr...
Modern business creates an increasing need for sharing, querying and mining informa-tion across auto...
A common goal of privacy research is to release synthetic data that satisfies a formal privacy guara...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
Linear queries can be submitted to a server containing private data. The server provides a response ...
We study the classical problem of privacy amplification, where two parties Alice and Bob share a wea...
We study the classical problem of privacy amplification, where two parties Alice and Bob share a wea...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We consider the problem of minimizing the communication in single-database private information retri...
Private Information Retrieval (PIR), despite being well studied, is computationally costly and arduo...
Abstract. In previous work, we presented a novel information-theoretic privacy criterion for query f...
Recommendation systems are information-filtering systems that tailor information to users on the bas...
Recommendation systems are information-filtering systems that tailor information to users on the bas...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Abstract—We address the problem of query profile obfuscation by means of partial query exchanges bet...
Abstract. The notion of differential privacy has emerged in the area of statisti-cal databases to pr...
Modern business creates an increasing need for sharing, querying and mining informa-tion across auto...
A common goal of privacy research is to release synthetic data that satisfies a formal privacy guara...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
Linear queries can be submitted to a server containing private data. The server provides a response ...
We study the classical problem of privacy amplification, where two parties Alice and Bob share a wea...
We study the classical problem of privacy amplification, where two parties Alice and Bob share a wea...
Differential privacy is a framework to quantify to what extent individual privacy in a statistical d...
We consider the problem of minimizing the communication in single-database private information retri...
Private Information Retrieval (PIR), despite being well studied, is computationally costly and arduo...