Towards a Privacy-Aware Quantified Self Data Management Framework

被引：11

作者：

Thuraisingham, Bhavani ^{[1
]}

Kantarcioglu, Murat ^{[1
]}

Bertino, Elisa ^{[2
]}

Bakdash, Jonathan Z. ^{[3
]}

Fernandez, Maribel ^{[4
]}

机构：

[1] Univ Texas Dallas, 800 W Campbell Rd, Richardson, TX 75083 USA

[2] Purdue Univ, 610 Purdue Mall, W Lafayette, IN 47907 USA

[3] Univ Texas Dallas, Field Element, US Army Res Lab, 800 W Campbell Rd, Richardson, TX 75083 USA

[4] Univ London, Kings Coll, London WC2R 2LS, England

来源：

SACMAT'18: PROCEEDINGS OF THE 23RD ACM SYMPOSIUM ON ACCESS CONTROL MODELS & TECHNOLOGIES | 2018年

基金：

美国国家科学基金会;

关键词：

Data privacy; quantified self; privacy preserving; data analytics; POLICY;

D O I：

10.1145/3205977.3205997

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Massive amounts of data are being collected, stored, and analyzed for various business and marketing purposes. While such data analysis is critical for many applications, it could also violate the privacy of individuals. This paper describes the issues involved in designing a privacy aware data management framework for collecting, storing, and analyzing the data. We also discuss behavioral aspects of data sharing as well as aspects of a formal framework based on rewriting rules that encompasses the privacy aware data management framework.

引用

页码：173 / 184

页数：12

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