Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Ryan McKenna"'
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 13, Iss 1 (2023)
In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a differentially private algorithm for answering a workload of predicate counting queries. HDMM represents query workloads using a compact implicit matrix representation and explo
Externí odkaz:
https://doaj.org/article/2b2d3aaec79144f6bc1349ecf7066b52
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 11, Iss 3 (2021)
We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with a noise addition mechanism, and (3) generate synt
Externí odkaz:
https://doaj.org/article/dde4f43d674242f98e65be5d0ed38452
Autor:
Michael Hay, George Bissias, Ios Kotsogiannis, Dan Zhang, Gerome Miklau, Ryan McKenna, Ashwin Machanavajjhala
Publikováno v:
ACM Transactions on Database Systems. 45:1-44
The adoption of differential privacy is growing, but the complexity of designing private, efficient, and accurate algorithms is still high. We propose a novel programming framework and system, ϵ KTELO for implementing both existing and new privacy a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9950c5b793e4aa1f710bd626ffeb07d
https://doi.org/10.1145/3362032
https://doi.org/10.1145/3362032
We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::448bb631c3e491fd328fa0d50dfbb7d9
http://arxiv.org/abs/2201.12677
http://arxiv.org/abs/2201.12677
Autor:
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Publikováno v:
ACM SIGMOD Record. 48:15-22
The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, ∈ktelo, for implementing both existing and new privacy al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cec20ba57caebb023283abaa0b560b99
https://doi.org/10.1145/3371316.3371321
https://doi.org/10.1145/3371316.3371321
We propose a new mechanism to accurately answer a user-provided set of linear counting queries under local differential privacy (LDP). Given a set of linear counting queries (the workload) our mechanism automatically adapts to provide accuracy on the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42a669636e8478d056d543c30555169c
http://arxiv.org/abs/2002.01582
http://arxiv.org/abs/2002.01582
Publikováno v:
FAT*
Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7a99a2a68c1df6b29fb9bc760e39646
Publikováno v:
Academic Pain Medicine ISBN: 9783030180041
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::095b64f8cdb532109900ff600578efb1
https://doi.org/10.1007/978-3-030-18005-8_41
https://doi.org/10.1007/978-3-030-18005-8_41
Autor:
Ryan McKenna, Cagri Sahin, James Clause, Mian Wan, Zachary Pearson, Philip Tornquist, William G. J. Halfond
Publikováno v:
Journal of Software: Evolution and Process. 28:565-588
Software piracy is an important concern for application developers. Such concerns are especially relevant in mobile application development, where piracy rates can be greater than 90%. The most common approach used by mobile developers to prevent pir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11ef843ddc2d15282ea3b7d64da66adb
https://doi.org/10.1002/smr.1762
https://doi.org/10.1002/smr.1762