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Verifying opacity by abstract interpretation

Published:06 May 2022Publication History

ABSTRACT

Nowadays, preventing sensitive information leakage is a crucial issue. Indeed, in order to deploy secure computing systems, data protection is an aspect that cannot be ignored. In this respect, opacity is a security policy aiming at hiding the truth value of a predicate during computation. Unfortunately, despite its simple intended meaning, opacity is a quite difficult program property (indeed it is actually an hyperproperty) to guarantee. In this paper, we propose a verification mechanism, based on abstract interpretation, for opacity. Indeed, while studying the relation between opacity and abstract non-interference, a weakening of non-interference observing properties of program computations instead of concrete values, we noticed that under particular conditions opacity is implied by abstract non-interference. Hence, by exploiting the recently proposed static approach for verifying non-interference, based on hypersemantics, we can show how to verify abstract non-interference and therefore opacity.

References

  1. M. Assaf, D. A. Naumann, J. Signoles, E. Totel, and F. Tronel. 2017. Hypercollecting semantics and its application to static analysis of information flow. In Proc. of POPL. 874--887.Google ScholarGoogle Scholar
  2. J. W. Bryans, M. Koutny, L. Mazaré, and P. Y. A. Ryan. 2008. Opacity generalised to transition systems. Int. J. Inf. Sec. 7, 6 (2008), 421--435.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. R. Clarkson and F. B. Schneider. 2010. Hyperproperties. Journal of Computer Security 18, 6 (2010), 1157--1210.Google ScholarGoogle ScholarCross RefCross Ref
  4. P. Cousot. 2002. Constructive design of a hierarchy of semantics of a transition system by abstract interpretation. Theor. Comput. Sci. 277, 1--2 (2002), 47--103.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Cousot and R. Cousot. 1977. Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints. In Proc. of POPL. 238--252.Google ScholarGoogle Scholar
  6. M. Dalla Preda and Roberto Giacobazzi. 2005. Semantic-Based Code Obfuscation by Abstract Interpretation. In Proc. of ICALP. 1325--1336.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Dalla Preda and I. Mastroeni. 2018. Characterizing a property-driven obfuscation strategy. J. Comput. Secur. 26, 1 (2018), 31--69.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. Giacobazzi and I. Mastroeni. 2004. Abstract Non-interference: Parameterizing Non-Interference by Abstract Interpretation. In Proc. of POPL. 186--197.Google ScholarGoogle Scholar
  9. R. Giacobazzi and I. Mastroeni. 2018. Abstract Non-Interference: A Unifying Framework for Weakening Information-flow. ACM Trans. Priv. Secur. 21, 2 (2018), 1--31.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. R. Giacobazzi, F. Ranzato, and F. Scozzari. 2000. Making Abstract Interpretation Complete. Journal of the ACM 47, 2 (2000), 361--416.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. J. D. Hughes and V. Shmatikov. 2004. Information Hiding, Anonymity and Privacy: a Modular Approach. J. Comput. Secur. 12, 1 (2004), 3--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. Hunt and I. Mastroeni. 2005. The PER Model of Abstract Non-interference. In Proc. of SAS. 171--185.Google ScholarGoogle Scholar
  13. I. Mastroeni and M. Pasqua. 2017. Hyperhierarchy of Semantics - A Formal Framework for Hyperproperties Verification. In Proc. of SAS. 232--252.Google ScholarGoogle Scholar
  14. I. Mastroeni and M. Pasqua. 2018. Verifying Bounded Subset-Closed Hyperproperties. In Proc. of SAS. 1--20.Google ScholarGoogle Scholar
  15. I. Mastroeni and M. Pasqua. 2019. Statically analyzing information flows: an abstract interpretation-based hyperanalysis for non-interference. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019. 2215--2223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. F. Ranzato and F. Tapparo. 2004. Strong Preservation as Completeness in Abstract Interpretation. In Proc. of ESOP. 18--32.Google ScholarGoogle Scholar
  17. P.Y.A. Ryan and T. Peacock. 2006. Opacity - Further Insights on an Information Flow Property. Technical Report CS-TR-958, University of Newcastle upon Tyne.Google ScholarGoogle Scholar
  18. D. Schoepe and A. Sabelfeld. 2015. Understanding and Enforcing Opacity. In Proc. of CSF. 539--553.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Conferences
        SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
        April 2022
        2099 pages
        ISBN:9781450387132
        DOI:10.1145/3477314

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        Publication History

        • Published: 6 May 2022

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