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Kobbi Nissim
The Limits of Post-Selection Generalization
Individuals and Privacy in the Eye of Data Analysis
Calibrating Noise to Sensitivity in Private Data Analysis
Differential Privacy, Property Testing, and Perturbations
Amicus Brief of Data Privacy Experts
Calibrating Noise to Sensitivity in Private Data Analysis
Efficiently Querying Databases While Providing Differential Privacy
Towards Formalizing the GDPR's Notion of Singling
Differentially Private Deep Learning with Smooth Sensitivity
The Gödel Prize 2020 - Call for Nominatonn
Smooth Sensitivity and Sampling in Private Data Analysis∗
Printable Format
ACM SIGACT Announces 2017 Awards Gödel, Knuth and Distinguished Service Prizes Recognize Contributions to Algorithms and Compu
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
2020 Knuth Prize Is Awarded to Cynthia Dwork
Pure Differential Privacy for Rectangle Queries Via Private Partitions
Differentially Private Release and Learning of Threshold Functions
Lipics-ICALP-2016-29.Pdf (0.6
Top View
Appendices Appendix 1: Curriculum Vitae Cvs Including Education
The Power of Synergy in Differential Privacy:Combining a Small Curator with Local Randomizers
Analysis of Perturbation Techniques in Online Learning
The Limits of Two-Party Differential Privacy
Simultaneous Private Learning of Multiple Concepts∗
Salil P. Vadhan
Differential Privacy: What, Why and When a Tutorial
Calibrating Noise to Sensitivity in Private Data Analysis
Max-Information, Differential Privacy, and Post-Selection
Closure Properties for Private Classification and Online Prediction
Designing Access with Differential Privacy
Differential Privacy: a Primer for a Non-Technical Audience
2017 Gödel Prize
Mathematisches Forschungsinstitut Oberwolfach
Arxiv:1702.02970V1 [Cs.DS] 9 Feb 2017
THE PINHAS SAPIR CENTER for DEVELOPMENT TEL AVIV UNIVERSITY Approximately Optimal Mechanism Design Via Differential Privacy Ko
Truthful Mechanisms for Agents That Value Privacy
Approximately Optimal Mechanism Design Via Differential Privacy
The Implications of Privacy-Aware Choice
Separating Local & Shuffled Differential Privacy Via Histograms
A Computational Separation Between Private Learning and Online Learning∗
Clustering Algorithms for the Centralized and Local Models∗
Differential Privacy Under Continual Observation
Lipics-FORC-2020-4.Pdf (0.5
An Equivalence Between Private Classification and Online Prediction
PSI (Ψ): a Private Data Sharing Interface∗ (Working Paper)
Pure Vs. Approximate Differential Privacy
Salil P. Vadhan
Database Theory – ICDT 2005
On the Geometry of Two-Party Differentially-Private Protocols
Privately Learning Thresholds: Closing the Exponential Gap
Bridging the Gap Between Computer Science and Legal Approaches to Privacy
Final Program & Abstracts
Differentially Private Release and Learning of Threshold Functions
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
UNITED STATES DISTRICT COURT for the MIDDLE DISTRICT of ALABAMA EASTERN DIVISION the STATE of ALABAMA, Et Al. Plaintiffs, V
On Thesemantics' of Differential Privacy: a Bayesian Formulation