Results 241 to 250 of about 24,256,356 (300)
Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme. [PDF]
Sci RepNyangaresi VO +8 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Computer, 2014
Big data's explosive growth has prompted the US government to release new reports that address the issues--particularly related to privacy--resulting from this growth. The Web extra at http://youtu.be/j49eoe5g8-c is an audio recording from the Computing and the Law column, in which authors Brian M.
Brian M. Gaff +2 more
openaire +1 more source
Big data's explosive growth has prompted the US government to release new reports that address the issues--particularly related to privacy--resulting from this growth. The Web extra at http://youtu.be/j49eoe5g8-c is an audio recording from the Computing and the Law column, in which authors Brian M.
Brian M. Gaff +2 more
openaire +1 more source
Computer, 2012
The third in a series of articles providing basic information on legal issues facing people and businesses that operate in computing-related markets focuses on the responsibility to ensure privacy and data security. The featured Web extra is an audio podcast by Brian M. Gaff and Thomas J. Smedinghoff, two of the article's coauthors.
Brian M. Gaff +2 more
openaire +2 more sources
The third in a series of articles providing basic information on legal issues facing people and businesses that operate in computing-related markets focuses on the responsibility to ensure privacy and data security. The featured Web extra is an audio podcast by Brian M. Gaff and Thomas J. Smedinghoff, two of the article's coauthors.
Brian M. Gaff +2 more
openaire +2 more sources
2011
In today's globally interconnected society, a huge amount of data about individuals is collected, processed, and disseminated. Data collections often contain sensitive personally identifiable information that need to be adequately protected against improper disclosure.
M. Bezzi +5 more
openaire +1 more source
In today's globally interconnected society, a huge amount of data about individuals is collected, processed, and disseminated. Data collections often contain sensitive personally identifiable information that need to be adequately protected against improper disclosure.
M. Bezzi +5 more
openaire +1 more source
2011
In modern digital society, personal information about individuals can be easily collected, shared, and disseminated. These data collections often contain sensitive information, which should not be released in association with respondents' identities.
S. De Capitani di Vimercati +2 more
openaire +3 more sources
In modern digital society, personal information about individuals can be easily collected, shared, and disseminated. These data collections often contain sensitive information, which should not be released in association with respondents' identities.
S. De Capitani di Vimercati +2 more
openaire +3 more sources
Communications of the ACM, 2010
Considering the nebulous question of ownership in the virtual realm.
Kieron O'Hara, Nigel Shadbolt
openaire +3 more sources
Considering the nebulous question of ownership in the virtual realm.
Kieron O'Hara, Nigel Shadbolt
openaire +3 more sources
Data Mining and Knowledge Discovery, 2005
In this chapter we describe the main tools for privacy in data mining. We present an overview of the tools for protecting data, and then we focus on protection procedures. Information loss and disclosure risk measures are also described.
Josep Domingo-Ferrer, Vicenç Torra
openaire +1 more source
In this chapter we describe the main tools for privacy in data mining. We present an overview of the tools for protecting data, and then we focus on protection procedures. Information loss and disclosure risk measures are also described.
Josep Domingo-Ferrer, Vicenç Torra
openaire +1 more source
2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science, 2015
Several important application areas in data science involve assigning numbers to (possibly randomized) algorithms. In the case of statistical privacy, it is important to quantify the amount of information leaked by a data processing algorithm. In the case of data marketplaces, it is important to properly set the prices for data queries (which, in ...
openaire +1 more source
Several important application areas in data science involve assigning numbers to (possibly randomized) algorithms. In the case of statistical privacy, it is important to quantify the amount of information leaked by a data processing algorithm. In the case of data marketplaces, it is important to properly set the prices for data queries (which, in ...
openaire +1 more source