M
Mikhail J. Atallah
Researcher at Purdue University
Publications - 331
Citations - 14580
Mikhail J. Atallah is an academic researcher from Purdue University. The author has contributed to research in topics: Parallel algorithm & Digital watermarking. The author has an hindex of 63, co-authored 330 publications receiving 14019 citations. Previous affiliations of Mikhail J. Atallah include Johns Hopkins University & Research Institute for Advanced Computer Science.
Papers
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Book
Algorithms and Theory of Computation Handbook
TL;DR: This edition now covers external memory, parameterized, self-stabilizing, and pricing algorithms as well as the theories of algorithmic coding, privacy and anonymity, databases, computational games, and communication networks.
Proceedings ArticleDOI
Secure multi-party computation problems and their applications: a review and open problems
Wenliang Du,Mikhail J. Atallah +1 more
TL;DR: A framework is developed to identify and define a number of new SMC problems for a spectrum of computation domains that include privacy-preserving database query, privacy- Preserving scientific computations, Privacy-Preserving intrusion detection,privacy-preserve statistical analysis, privacy -preserving geometric computation, and privacy- preserving data mining.
Proceedings ArticleDOI
Disclosure limitation of sensitive rules
TL;DR: This paper attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemets.
Journal ArticleDOI
Internet Addiction: Metasynthesis of 1996–2006 Quantitative Research
Sookeun Byun,Celestino Ruffini,Juline E. Mills,Alecia C. Douglas,Mamadou Niang,Svetlana Stepchenkova,Seul Ki Lee,Jihad Loutfi,Jung-Kook Lee,Mikhail J. Atallah,Marina Blanton +10 more
TL;DR: The analysis showed that previous studies have utilized inconsistent criteria to define Internet addicts, applied recruiting methods that may cause serious sampling bias, and examined data using primarily exploratory rather than confirmatory data analysis techniques to investigate the degree of association rather than causal relationships among variables.
Journal ArticleDOI
Dynamic and Efficient Key Management for Access Hierarchies
TL;DR: The security of the scheme is based on pseudorandom functions, without reliance on the Random Oracle Model, and it is shown how to handle extensions proposed by Crampton [2003] of the standard hierarchies to “limited depth” and reverse inheritance.