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@inproceedings{BEHLMQ18,
  title={Scalable Approximation of Quantitative Information Flow in Programs},
  author={
    Biondi, Fabrizio and Enescu, Michael and Heuser, Annelie and Legay, Axel and
    Meel, Kuldeep S. and Quilbeuf, Jean
  },
  booktitle=VMCAI,
  year={2018},
  month=jan,
  bib2html_rescat={Software Engineering},
  abstract={
    Quantitative information flow measurement techniques have
    been proven to be successful in detecting leakage of confidential
    information
    from programs. Modern approaches are based on formal methods,
    relying on program analysis to produce a SAT formula representing the
    program's behavior, and model counting to measure the possible information
    flow. However, while program analysis scales to large codebases
    like the OpenSSL project, the formulas produced are too complex for
    analysis with precise model counting. In this paper we use the approximate
    model counter ApproxMC2 to quantify information flow. We show
    that ApproxMC2 is able to provide a large performance increase for a very
    small loss of precision, allowing the analysis of SAT formulas produced
    from complex code. We call the resulting technique ApproxFlow and test it
    on a large set of benchmarks against the state of the art. Finally, we show
    that ApproxFlow can evaluate the leakage incurred by the Heartbleed
    OpenSSL bug, contrarily to the state of the art.
  },
  bib2html_dl_pdf={../Papers/vmcai18.pdf},
  bib2html_pubtype={Refereed Conference},
}