• Publications
  • Influence
Calibrating Noise to Sensitivity in Private Data Analysis
tl;dr
We extend the study 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. Expand
  • 3,719
  • 764
  • Open Access
Mechanism Design via Differential Privacy
tl;dr
We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Expand
  • 1,449
  • 231
  • Open Access
Our Data, Ourselves: Privacy Via Distributed Noise Generation
tl;dr
In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. Expand
  • 1,045
  • 121
  • Open Access
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
  • F. McSherry
  • Computer Science
  • SIGMOD Conference
  • 29 June 2009
tl;dr
We report on the design and implementation of the Privacy Integrated Queries (PINQ) platform for privacy-preserving data analysis. Expand
  • 780
  • 94
  • Open Access
Spectral partitioning of random graphs
  • F. McSherry
  • Computer Science
  • Proceedings IEEE International Conference on…
  • 14 October 2001
tl;dr
We show that a spectral algorithm can solve all three problems above in the average case, as well as a more general problem of partitioning graphs based on edge density. Expand
  • 549
  • 71
  • Open Access
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
  • 724
  • 69
  • Open Access
Naiad: a timely dataflow system
tl;dr
Naiad is a distributed system for executing data parallel, cyclic dataflow programs. Expand
  • 608
  • 64
  • Open Access
On profit-maximizing envy-free pricing
tl;dr
We study the problem of pricing items for sale to consumers so as to maximize the seller's revenue by finding envy-free prices that maximize seller profit and at the same time are envy free. Expand
  • 314
  • 56
  • Open Access
Differentially private recommender systems: building privacy into the net
tl;dr
We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Expand
  • 587
  • 51
  • Open Access
A Simple and Practical Algorithm for Differentially Private Data Release
tl;dr
We present a new algorithm for differentially private data release, based on a simple combination of the Multiplicative Weights update rule with the Exponential Mechanism. Expand
  • 307
  • 43
  • Open Access