K
Krishnaram Kenthapadi
Researcher at Amazon.com
Publications - 191
Citations - 6696
Krishnaram Kenthapadi is an academic researcher from Amazon.com. The author has contributed to research in topics: Computer science & Web search query. The author has an hindex of 27, co-authored 177 publications receiving 5134 citations. Previous affiliations of Krishnaram Kenthapadi include LinkedIn & Stanford University.
Papers
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Book ChapterDOI
Our data, ourselves: privacy via distributed noise generation
TL;DR: In this paper, a distributed protocol for generating shares of random noise, secure against malicious participants, was proposed, where the purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers.
Journal Article
Our data, ourselves : Privacy via distributed noise generation
TL;DR: 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.
Proceedings ArticleDOI
Achieving anonymity via clustering
Gagan Aggarwal,Tomás Feder,Krishnaram Kenthapadi,Samir Khuller,Rina Panigrahy,Dilys Thomas,An Zhu +6 more
TL;DR: This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k, and extends the algorithms to allow an ε fraction of points to remain unclustered, i.e., deleted from the anonymized publication.
Book ChapterDOI
Anonymizing tables
Gagan Aggarwal,Tomás Feder,Krishnaram Kenthapadi,Rajeev Motwani,Rina Panigrahy,Dilys Thomas,An Zhu +6 more
TL;DR: In this article, the problem of k-anonymization was shown to be NP-hard, even when the attribute values are ternary, and an O(k)-approximation algorithm for the problem was given.
Proceedings Article
Two Can Keep a Secret: A Distributed Architecture for Secure Database Services
Gagan Aggarwal,Mayank Bawa,Prasanna Ganesan,Hector Garcia-Molina,Krishnaram Kenthapadi,Rajeev Motwani,Utkarsh Srivastava,Dilys Thomas,Ying Xu +8 more
TL;DR: This work proposes a new, distributed architecture that allows an organization to outsource its data management to untrusted servers while preserving data privacy, and shows how the presence of two servers enables efficient partitioning of data.