Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Joseph P. Near"'
Publikováno v:
Proceedings of the ACM on Programming Languages. 6:699-728
All current approaches for statically enforcing differential privacy in higher order languages make use of either linear or relational refinement types. A barrier to adoption for these approaches is the lack of support for expressing these "fancy typ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88b807db2ab2c94f20c510aa4a742366
https://doi.org/10.1145/3563313
https://doi.org/10.1145/3563313
Autor:
Vanessa White, Joseph P. Near, Christian Skalka, Colin M. Van Oort, John H. Ring, Samson Durst
Publikováno v:
Digital Threats: Research and Practice. 2:1-29
Host-based Intrusion Detection Systems (HIDS) automatically detect events that indicate compromise by adversarial applications. HIDS are generally formulated as analyses of sequences of system events such as bash commands or system calls. Anomaly-bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::121cc864f9469181e6b4070a5db1d731
https://doi.org/10.1145/3461462
https://doi.org/10.1145/3461462
Publikováno v:
CCS
Static program analysis tools can automatically prove many useful properties of programs. However, using static analysis to prove to a third party that a program satisfies a property requires revealing the program's source code. We introduce the conc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d23cb670f094384c0a462044e6a9d103
https://doi.org/10.1145/3460120.3484795
https://doi.org/10.1145/3460120.3484795
Publikováno v:
CSF
Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizing differential privacy have frequently taken the form of programming
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::055ad0dbcceca87bfa6f4ab9948e9ad3
https://doi.org/10.1109/csf51468.2021.00043
https://doi.org/10.1109/csf51468.2021.00043
Publikováno v:
2020 IEEE European Symposium on Security and Privacy (EuroS&P).
Differential privacy is fast becoming the gold standard in enabling statistical analysis of data while protecting the privacy of individuals. However, practical use of differential privacy still lags behind research progress because research prototyp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3239ddbeb9458a5df77a020ff4227ce7
https://doi.org/10.1109/eurosp48549.2020.00041
https://doi.org/10.1109/eurosp48549.2020.00041
Autor:
Matías Toro, David Darais, Chike Abuah, Joseph P. Near, Damián Árquez, Federico Olmedo, Éric Tanter
Language support for differentially-private programming is both crucial and delicate. While elaborate program logics can be very expressive, type-system based approaches using linear types tend to be more lightweight and amenable to automatic checkin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1712f87eb7e2399771c5b93ea1e3bc5c
Autor:
Joseph P. Near, Xi He
Differential privacy is a promising approach to formalizing privacy—that is, for writing down what privacy means as a mathematical equation. This book serves as an overview of the state-of-the-art in techniques for differential privacy. The authors
Publikováno v:
IEEE Symposium on Security and Privacy
Building useful predictive models often involves learning from sensitive data. Training models with differential privacy can guarantee the privacy of such sensitive data. For convex optimization tasks, several differentially private algorithms are kn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::708e23acb919bf9dd0027ef06cdab3fb
https://doi.org/10.1109/sp.2019.00001
https://doi.org/10.1109/sp.2019.00001
Autor:
Joseph P. Near, Alex Shan, Pranav Gaddamadugu, David Darais, Dawn Song, Tim Stevens, Mu Zhang, Neel Somani, Chike Abuah, Nikhil Sharma, Lun Wang
During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e67b0226330786370ad87a49dca0f51a