Zobrazeno 1 - 10
of 403
pro vyhledávání: '"Stephen E. Fienberg"'
Autor:
Stephen E. Fienberg
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 7, Iss 1 (2015)
This issue of the Journal of Privacy and Confidentiality consists four papers on diverse topics related to privacy and confidentiality.
Externí odkaz:
https://doaj.org/article/027cf56f269246d9ad3362744a1b9592
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 5, Iss 1 (2013)
Traditional statistical methods for confidentiality protection of statistical databases do not scale well to deal with GWAS databases especially in terms of guarantees regarding protection from linkage to external information. The more recent concept
Externí odkaz:
https://doaj.org/article/c74141074d8b44d891c5a5d5144565ba
Autor:
Stephen E. Fienberg
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 4, Iss 2 (2013)
While social networks are now a part of everyday life for the vast majority of people using computers, smartphones, and tablets, privacy is but an afterthought. Google+ has in excess of 100 million users a month while Facebook has topped 1 billion. O
Externí odkaz:
https://doaj.org/article/1b03835c10ce4212983ae104793cb9f8
Autor:
Stephen E. Fienberg, Jiashun Jin
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 4, Iss 1 (2012)
We focus on the problem of multi-party data sharing in high dimensional data settings where the number of measured features (or the dimension) p is frequently much larger than the number of subjects (or the sample size) n, the so-called p >> n scenar
Externí odkaz:
https://doaj.org/article/56a1b3afc97b4d35a004519bf05b238a
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 4, Iss 1 (2012)
Preserving the privacy of individual databases when carrying out statistical calculations has a relatively long history in statistics and had been the focus of much recent attention in machine learning. In this paper, we present a protocol for fittin
Externí odkaz:
https://doaj.org/article/76fc3be1ecd34319a64a1ce246a92b5a
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 4, Iss 1 (2012)
The methodology of differential privacy has provided a strong definition of privacy which in some settings, using a mechanism of doubly-exponential noise addition, also allows for extraction of informative statistics from databases. In a recent paper
Externí odkaz:
https://doaj.org/article/f857f9498f6a49ada6ac4989ec536c6c
Autor:
Stephen E. Fienberg
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 3, Iss 2 (2011)
Externí odkaz:
https://doaj.org/article/51306aed29f041938d1c4f95b58b5735
Autor:
Stephen E. Fienberg
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 3, Iss 2 (2011)
This issue of the Journal of Privacy and Confidentiality is built around a single paper by Gerald W. Gates, “How Uncertainty about Privacy and Confidentiality Is Hampering Efforts to More Effectively Use Administrative Records in Producing U.S. Nat
Externí odkaz:
https://doaj.org/article/83934c93faae43debcadd9c4cbc6b0dc
Autor:
Stephen E. Fienberg
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 3, Iss 1 (2011)
This issue of the Journal of Privacy and Confidentiality focuses on privacy in com- mercial and public data. Issues regarding the privacy of data collected online and from mobile devices appear in the news almost daily. As Facebook has grown to encom
Externí odkaz:
https://doaj.org/article/6f9b7a2df6ba4d9f9893947f45318b16
Autor:
Stephen E. Fienberg
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 2, Iss 1 (2010)
In this issue of the Journal of Privacy and Confidentiality , we continue our focus on the technical aspects of research methodologies and activities in the areas of privacy, confidentiality, and disclosure limitation through a trio of articles, but
Externí odkaz:
https://doaj.org/article/a35a864a28b64f719114117a83378ac0