Random projections for increased privacy

Jassim, Sabah A. (2017) Random projections for increased privacy. In: User-Centric Privacy and Security in Biometrics. Institution of Engineering and Technology. ISBN 9781785612084

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The extraordinary speed with which new models of communication and computing technologies have advanced over the last few years is mind boggling. New exciting opportunities are emerging all the time to facilitate high volume of global commercial activities, enable the citizens to enjoy convenient services as well as mobile leisure activities. These exciting opportunities and benefits come with increased concerns about security and privacy due to a plethora of reasons mostly caused by blurring of control over own data. Conventional access control to personal/organisational data assets use presumed reliable and secure mechanisms including biometric authentication, but little attention is paid to privacy of participants. Moreover, digitally stored files of online transactions include traceable personal data/reference. Recent increase in serious hacking incidents deepens the perception of lack of privacy. The emerging concept of personal and biometric data de-identification seem to provide the most promising approach to deal with this challenge. This chapter is concerned with constructing and using personalised random projections (RPs) for secure transformation of biometric templates into a domain from which it is infeasible to retrieve the owner identity. We shall describe the implications of the rapid changes in communication models on the characteristics of privacy, and describe the role that RP is, and can, play within biometric data de-identification for improved privacy in general and for cloud services in particular.

Item Type: Book Section
Uncontrolled Keywords: message authentication; data privacy; computer crime; biometrics (access control); mobile computing; cloud computing, data privacy; computer crime;
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Rachel Pollard
Date Deposited: 01 Aug 2019 10:42
Last Modified: 01 Aug 2019 10:42
URI: http://bear.buckingham.ac.uk/id/eprint/392

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