Policy auditing over incomplete logs: theory, implementation and applications

  • Authors:

  • Deepak Garg;Limin Jia;Anupam Datta

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Moffett Field, CA, USA

  • Venue:
  • Proceedings of the 18th ACM conference on Computer and communications security
  • Year:
  • 2011

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Abstract

We present the design, implementation and evaluation of an algorithm that checks audit logs for compliance with privacy and security policies. The algorithm, which we name reduce, addresses two fundamental challenges in compliance checking that arise in practice. First, in order to be applicable to realistic policies, reduce operates on policies expressed in a first-order logic that allows restricted quantification over infinite domains. We build on ideas from logic programming to identify the restricted form of quantified formulas. The logic can, in particular, express all 84 disclosure-related clauses of the HIPAA Privacy Rule, which involve quantification over the infinite set of messages containing personal information. Second, since audit logs are inherently incomplete (they may not contain sufficient information to determine whether a policy is violated or not), reduce proceeds iteratively: in each iteration, it provably checks as much of the policy as possible over the current log and outputs a residual policy that can only be checked when the log is extended with additional information. We prove correctness, termination, time and space complexity results for reduce. We implement reduce and optimize the base implementation using two heuristics for database indexing that are guided by the syntactic structure of policies. The implementation is used to check simulated audit logs for compliance with the HIPAA Privacy Rule. Our experimental results demonstrate that the algorithm is fast enough to be used in practice.