K
Krishna Sampigethaya
Researcher at University of Washington
Publications - 39
Citations - 2025
Krishna Sampigethaya is an academic researcher from University of Washington. The author has contributed to research in topics: Air traffic control & Avionics. The author has an hindex of 19, co-authored 37 publications receiving 1879 citations. Previous affiliations of Krishna Sampigethaya include Embry–Riddle Aeronautical University & Boeing Phantom Works.
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CARAVAN: Providing Location Privacy for VANET
TL;DR: This paper proposes a location privacy scheme called CARAVAN, and evaluates the privacy enhancement achieved under some existing standard constraints of VANET applications, and in the presence of a global adversary.
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AMOEBA: Robust Location Privacy Scheme for VANET
TL;DR: This paper addresses the problem of mitigating unauthorized tracking of vehicles based on their broadcast communications, to enhance the user location privacy in VANET with a scheme called AMOEBA, that provides location privacy by utilizing the group navigation of vehicles.
Proceedings ArticleDOI
Swing & swap: user-centric approaches towards maximizing location privacy
TL;DR: A user-centric scheme called Swing is proposed that increases location privacy by enabling the nodes to loosely synchronize updates when changing their velocity, and an approach called Swap is introduced that enables the node to exchange their identifiers to potentially maximize the location privacy provided by each update, hence reducing the number of updates needed to meet the desired privacy levels.
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Aviation Cyber–Physical Systems: Foundations for Future Aircraft and Air Transport
TL;DR: A novel cyber-physical system (CPS) framework is proposed to understand the cyber layer and cyber- physical interactions in aviation, study their impacts, and identify valuable research directions.
Journal ArticleDOI
A framework and taxonomy for comparison of electronic voting schemes
TL;DR: This paper provides a framework that classifies these approaches to electronic voting and defines a set of metrics under which their properties can be compared and reveals important differences in security properties between the classes.