V
Vitaly Shmatikov
Researcher at Cornell University
Publications - 153
Citations - 22828
Vitaly Shmatikov is an academic researcher from Cornell University. The author has contributed to research in topics: Anonymity & Information privacy. The author has an hindex of 64, co-authored 148 publications receiving 17801 citations. Previous affiliations of Vitaly Shmatikov include University of Texas at Austin & French Institute for Research in Computer Science and Automation.
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Proceedings ArticleDOI
Robust De-anonymization of Large Sparse Datasets
TL;DR: This work applies the de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service, and demonstrates that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset.
Proceedings ArticleDOI
Membership Inference Attacks Against Machine Learning Models
TL;DR: This work quantitatively investigates how machine learning models leak information about the individual data records on which they were trained and empirically evaluates the inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon.
Proceedings ArticleDOI
Privacy-Preserving Deep Learning
Reza Shokri,Vitaly Shmatikov +1 more
TL;DR: This paper presents a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets, and exploits the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously.
Proceedings ArticleDOI
De-anonymizing Social Networks
TL;DR: A framework for analyzing privacy and anonymity in social networks is presented and a new re-identification algorithm targeting anonymized social-network graphs is developed, showing that a third of the users who can be verified to have accounts on both Twitter and Flickr can be re-identified in the anonymous Twitter graph.
Proceedings ArticleDOI
Exploiting Unintended Feature Leakage in Collaborative Learning
TL;DR: In this article, passive and active inference attacks are proposed to exploit the leakage of information about participants' training data in federated learning, where each participant can infer the presence of exact data points and properties that hold only for a subset of the training data and are independent of the properties of the joint model.