Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Ios Kotsogiannis"'
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
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
Autor:
Michael Hay, Gerome Miklau, Maryam Fanaeepour, Xi He, Yuchao Tao, Ios Kotsogiannis, Ashwin Machanavajjhala
Publikováno v:
Proceedings of the VLDB Endowment. 12:1371-1384
Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::823e9c9bb776fbb55afeb4d0eb862151
https://doi.org/10.14778/3342263.3342274
https://doi.org/10.14778/3342263.3342274
Autor:
George Bissias, Ashwin Machanavajjhala, Michael Hay, Dan Zhang, Ios Kotsogiannis, Ryan McKenna, Gerome Miklau
Publikováno v:
Proceedings of the 2018 International Conference on Management of Data.
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, Ektelo, for implementing both existing and new privacy algo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::112b05386e852fb6cc0f3815343f9c00
https://doi.org/10.1145/3183713.3196921
https://doi.org/10.1145/3183713.3196921
Publikováno v:
ICDE
In this paper, we study the problem of privacy-preserving data sharing, wherein only a subset of the records in a database are sensitive, possibly based on predefined privacy policies. Existing solutions, viz, differential privacy (DP), are over-pess
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::850be42cadb14f225a8bbc1a62bbc899
http://arxiv.org/abs/1712.05888
http://arxiv.org/abs/1712.05888
Publikováno v:
SIGMOD Conference
Differential privacy has emerged as the dominant privacy standard for data analysis. Its wide acceptance has led to significant development of algorithms that meet this rigorous standard. For some tasks, such as the task of answering low dimensional
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1b2b65be330bcf4564de2136423ff6e
https://doi.org/10.1145/3035918.3056441
https://doi.org/10.1145/3035918.3056441
Publikováno v:
SIGMOD Conference
Differential privacy has emerged as a preferred standard for ensuring privacy in analysis tasks on sensitive datasets. Recent algorithms have allowed for significantly lower error by adapting to properties of the input data. These so-called data-depe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3517b75ae22a7be4cf28a0d6e2578ac
https://doi.org/10.1145/3035918.3035945
https://doi.org/10.1145/3035918.3035945
Publikováno v:
WSDM
Recommender systems have become ubiquitous in online applications where companies personalize the user experience based on explicit or inferred user preferences. Most modern recommender systems concentrate on finding relevant items for each individua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::162eebaf7569abf9c75cf9915f1cf0b5
https://doi.org/10.1145/3018661.3018729
https://doi.org/10.1145/3018661.3018729