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Timothy Stevens

dblp:135/8303 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorSecurity and privacy · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Privacy and data protection · 56% Cryptographic protocols and secure computation · 44%
Artificial intelligence
1 paper
Robot navigation and mapping · 77% Planning, search and constraint satisfaction · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection › privacy-preserving machine learning
federated learning privacy
0.612022
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors · USENIX Security Symposium 2022
Cryptographic protocols and secure computation
secure aggregation
0.612022
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors · USENIX Security Symposium 2022
Privacy and data protection
differential privacy
0.212022
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors · USENIX Security Symposium 2022
Robotics › Robot navigation and mapping › target search
autonomous search
0.212013
Autonomous search and counter-targeting using Levy search models · ICRA 2013
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
game tree search
0.012013
Autonomous search and counter-targeting using Levy search models · ICRA 2013

Methods — techniques the papers use, named apart from their topics

learning with errors · 0.6differential privacy · 0.6regression analysis · 0.2levy distribution · 0.2
YearPublicationVenuePosition
2022 Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors
Timothy Stevens, Christian Skalka, Christelle Vincent, John H. Ring, Samuel Clark, Joseph P. Near
USENIX Security Symposium1
2013 Autonomous search and counter-targeting using Levy search models
abstract
In this study, we explore the use of nondeterministic search trajectories to accomplish a two-fold mission of mobile robot search for a stationary target while avoiding counter-targeting by the adversary throughout the operation. We analyze the characteristics associated with a Levy distribution of search leg lengths to generate appropriate randomized search trajectories. We discuss the alteration of the probability distribution of the Levy search as a result of the method utilized to best address the presence of the bounded search area and confine the searcher within its boundaries. Through regression analysis of simulation results, we determine expressions for the coverage ratio evolution of the modified Levy search strategy and the distribution on time to target detection TD, from which we are able to calculate the expected time, E[TD], to detect the target uniformly distributed within the search area. We assert assumptions regarding the adversary's detection and tracking abilities to estimate the expected time, E[TC], required for it to counter target the searcher. From these two expected times, we construct a novel probabilistic mission performance metric that measures the likelihood that the searcher will detect the target before it is counter targeted itself.
Timothy Stevens, Timothy H. Chung
ICRA1