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Calibrating Noise to Sensitivity in Private Data Analysis
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.
The Algorithmic Foundations of Differential Privacy
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.
- C. Dwork
- Computer ScienceEncyclopedia of Cryptography and Security
- 10 July 2006
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.
Fairness through awareness
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.
Differential Privacy: A Survey of Results
- C. Dwork
- Computer ScienceTAMC
- 25 April 2008
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.
Rank aggregation methods for the Web
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.
Our Data, Ourselves: Privacy Via Distributed Noise Generation
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.
Consensus in the presence of partial synchrony
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.
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 the…
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.