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2014 IEEE Symposium on Security and Privacy (SP) (2014)
Berkeley, CA, USA
May 18, 2014 to May 21, 2014
ISSN: 1081-6011
ISBN: 978-1-4799-4686-0
pp: 327-342
With the rapid increase in cloud services collecting and using user data to offer personalized experiences, ensuring that these services comply with their privacy policies has become a business imperative for building user trust. However, most compliance efforts in industry today rely on manual review processes and audits designed to safeguard user data, and therefore are resource intensive and lack coverage. In this paper, we present our experience building and operating a system to automate privacy policy compliance checking in Bing. Central to the design of the system are (a) Legal ease-a language that allows specification of privacy policies that impose restrictions on how user data is handled, and (b) Grok-a data inventory for Map-Reduce-like big data systems that tracks how user data flows among programs. Grok maps code-level schema elements to data types in Legal ease, in essence, annotating existing programs with information flow types with minimal human input. Compliance checking is thus reduced to information flow analysis of Big Data systems. The system, bootstrapped by a small team, checks compliance daily of millions of lines of ever-changing source code written by several thousand developers.
Privacy, IP networks, Lattices, Data privacy, Advertising, Semantics, Big data

S. Sen, S. Guha, A. Datta, S. K. Rajamani, J. Tsai and J. M. Wing, "Bootstrapping Privacy Compliance in Big Data Systems," 2014 IEEE Symposium on Security and Privacy (SP), Berkeley, CA, USA, 2014, pp. 327-342.
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