• Publications
  • Influence
Calibrating Noise to Sensitivity in Private Data Analysis
TLDR
The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output. Expand
The Algorithmic Foundations of Differential Privacy
TLDR
The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. Expand
Differential Privacy
  • C. Dwork
  • Computer Science
  • ICALP
  • 10 July 2006
TLDR
A general impossibility result is given showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, which suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database. Expand
Rank aggregation methods for the Web
TLDR
A set of techniques for the rank aggregation problem is developed and compared to that of well-known methods, to design rank aggregation techniques that can be used to combat spam in Web searches. Expand
Fairness through awareness
TLDR
A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented. Expand
Differential Privacy: A Survey of Results
  • C. Dwork
  • Computer Science
  • TAMC
  • 25 April 2008
TLDR
This survey recalls the definition of differential privacy and two basic techniques for achieving it, and shows some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning. Expand
Consensus in the presence of partial synchrony
TLDR
Fault-tolerant consensus protocols are given for various cases of partial synchrony and various fault models that allow partially synchronous processors to reach some approximately common notion of time. Expand
Our Data, Ourselves: Privacy Via Distributed Noise Generation
TLDR
This work provides efficient distributed protocols for generating shares of random noise, secure against malicious participants, and introduces a technique for distributing shares of many unbiased coins with fewer executions of verifiable secret sharing than would be needed using previous approaches. Expand
Non-malleable cryptography
TLDR
Non-malleable schemes for each of the contexts of string commitment and zero-knowledge proofs of possession of knowledge, where a user need not know anything about the number or identity of other system users are presented. Expand
Learning Fair Representations
We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to theExpand
...
1
2
3
4
5
...