• Publications
  • Influence
Calibrating Noise to Sensitivity in Private Data Analysis
We continue a line of research initiated in [10, 11] on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function fExpand
  • 3,669
  • 751
Differential Privacy
  • C. Dwork
  • Computer Science
  • 10 July 2006
In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We giveExpand
  • 3,570
  • 427
The Algorithmic Foundations of Differential Privacy
  • C. Dwork, A. Roth
  • Computer Science
  • Found. Trends Theor. Comput. Sci.
  • 11 August 2014
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables everExpand
  • 2,160
  • 392
Rank aggregation methods for the Web
We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions,Expand
  • 1,700
  • 184
Consensus in the presence of partial synchrony
The concept of partial synchrony in a distributed system is introduced. Partial synchrony lies between the cases of a synchronous system and an asynchronous system. In a synchronous system, there isExpand
  • 1,372
  • 154
Differential Privacy: A Survey of Results
  • C. Dwork
  • Computer Science
  • TAMC
  • 25 April 2008
Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literatureExpand
  • 1,912
  • 151
Fairness through awareness
We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in someExpand
  • 972
  • 133
Our Data, Ourselves: Privacy Via Distributed Noise Generation
In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributedExpand
  • 1,035
  • 119
Non-malleable cryptography
The notion of non-malleable cryptography, an extension of semantically secure cryptography, is defined. Informally, the additional requirement is that given the ciphertext it is impossible toExpand
  • 1,008
  • 103
Practical privacy: the SuLQ framework
We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, aExpand
  • 723
  • 69