• Publications
  • Influence
Differential privacy under continual observation
TLDR
Differential privacy is a recent notion of privacy tailored to privacy-preserving data analysis [11]. Expand
  • 443
  • 61
  • PDF
Boosting and Differential Privacy
TLDR
Boosting is a general method for improving the accuracy of learning algorithms. Expand
  • 567
  • 53
  • PDF
A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
TLDR
We propose a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) queries that arrive online and may be adaptively chosen. Expand
  • 328
  • 27
  • PDF
Delegating Computation
TLDR
In this work we study interactive proofs for tractable languages. Expand
  • 324
  • 22
  • PDF
On the complexity of differentially private data release: efficient algorithms and hardness results
TLDR
We consider private data analysis in the setting in which a trusted and trustworthy curator, having obtained a large data set containing private information, releases to the public a "sanitization" of the data set that simultaneously protects the privacy of the individual contributors of data and offers utility to the data analyst. Expand
  • 307
  • 20
  • PDF
One-Time Programs
TLDR
We introduce one-time programs, a new computational paradigm geared towards security applications. Expand
  • 205
  • 17
  • PDF
Concentrated Differential Privacy
TLDR
We introduce Concentrated Differential Privacy, a relaxation of differential privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations. Expand
  • 187
  • 16
  • PDF
On Best-Possible Obfuscation
TLDR
An obfuscator is a compiler that transforms any program (which we will view in this work as a boolean circuit) into an obfuscated program (also a circuit) that has the same input-output functionality as the original program, but is "unintelligible". Expand
  • 131
  • 16
Pan-Private Streaming Algorithms
TLDR
We initiate a study of pan-private streaming algorithms; roughly speaking, these algorithms retain their privacy properties even if their internal state becomes visible to an adversary. Expand
  • 118
  • 14
  • PDF
Securely Obfuscating Re-Encryption
TLDR
We present a positive obfuscation result for a traditional cryptographic functionality for a re-encryption program. Expand
  • 149
  • 11
  • PDF