Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Aleksandra Slavkovic"'
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 12, Iss 2 (2022)
The stochastic block model (SBM) and degree-corrected block model (DCBM) are network models often selected as the fundamental setting in which to analyze the theoretical properties of community detection methods. We consider the problem of spectral c
Externí odkaz:
https://doaj.org/article/6d0b6e5ef57e4a49bb0049c1a5dc015d
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 12, Iss 1 (2022)
We present an approach to construct differentially private synthetic data for contingency tables. The algorithm achieves privacy by adding noise to selected summary counts, e.g., two-way margins of the contingency table, via the Geometric mechanism.
Externí odkaz:
https://doaj.org/article/ce789993a71a401f8d1ed1acc6e4bfe7
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 10, Iss 1 (2020)
We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance. We show that in general, DP hypothesis tests can be
Externí odkaz:
https://doaj.org/article/c91415d4f64740f59d8e79e855a18cce
Autor:
Alyssa Goodman, Alberto Pepe, Alexander W Blocker, Christine L Borgman, Kyle Cranmer, Merce Crosas, Rosanne Di Stefano, Yolanda Gil, Paul Groth, Margaret Hedstrom, David W Hogg, Vinay Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra Slavkovic
Publikováno v:
PLoS Computational Biology, Vol 10, Iss 4, p e1003542 (2014)
Externí odkaz:
https://doaj.org/article/5244d57974a94c6bbe422ea7c3b0061d
Publikováno v:
Scopus-Elsevier
Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carlo (MCMC) are used, the end result incurs costs to both privacy and acc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edac5ac8c44f784d31db4bbb2cef6f51
The stochastic block model (SBM) and degree-corrected block model (DCBM) are network models often selected as the fundamental setting in which to analyze the theoretical properties of community detection methods. We consider the problem of spectral c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b9b469bfc0ca5f9380bcacda02a059a
http://arxiv.org/abs/2105.12615
http://arxiv.org/abs/2105.12615
Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while prov
Autor:
John M. Abowd, Cynthia Dwork, Alan F. Karr, Kobbi Nissim, Jerome Reiter, Aleksandra Slavković, Lars Vilhuber
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 14, Iss 3 (2024)
We describe the launching of the Society for Privacy and Confidentiality Research (SPCR). SPCR is the new owner of the Journal of Privacy and Confidentiality, with the goal of ensuring a sustainable future for the Journal, and continuing to publish t
Externí odkaz:
https://doaj.org/article/1ebbd6f53eab49f08e253802db03add9
BACKGROUND In clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba2d427ccf3af6e24252136be44f8649
https://doi.org/10.2196/preprints.21459
https://doi.org/10.2196/preprints.21459
Autor:
Awan, J., Aleksandra Slavkovic
Publikováno v:
Scopus-Elsevier
We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance. We show that in general, DP hypothesis tests for ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53495c9d5284195d804fd40cc56b53e5