Roy, Sudeepa

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  • Publication
    Privacy Issues in Scientific Workflow Provenance
    (2010-06-01) Davidson, Susan B; Khanna, Sanjeev; Roy, Sudeepa; Cohen Boulakia, Sarah
    A scientific workflow often deals with proprietary modules as well as private or confidential data, such as health or medical information. Hence providing exact answers to provenance queries over all executions of the workflow may reveal private information. In this paper we first study the potential privacy issues in a scientific workflow – module privacy, data privacy, and provenance privacy, and frame several natural questions: (i) can we formally analyze module, data or provenance privacy giving provable privacy guarantees for an unlimited/bounded number of provenance queries? (ii) how can we answer provenance queries, providing as much information as possible to the user while still guaranteeing the required privacy? Then we look at module privacy in detail and propose a formal model from our recent work in [11]. Finally we point to several directions for future work.
  • Publication
    An Optimal Labeling Scheme for Workflow Provenance Using Skeleton Labels
    (2010-06-01) Bao, Zhuowei; Davidson, Susan B.; Khanna, Sanjeev; Roy, Sudeepa
    We develop a compact and efficient reachability labeling scheme for answering provenance queries on workflow runs that conform to a given specification. Even though a workflow run can be structurally more complex and can be arbitrarily larger than the specification due to fork (parallel) and loop executions, we show that a compact reachability labeling for a run can be efficiently computed using the fact that it originates from a fixed specification. Our labeling scheme is optimal in the sense that it uses labels of logarithmic length, runs in linear time, and answers any reachability query in constant time. Our approach is based on using the reachability labeling for the specification as an effective skeleton for designing the reachability labeling for workflow runs. We also demonstrate empirically the effectiveness of our skeleton-based labeling approach.