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Linear Program Reconstruction in Practice

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arxiv 1810.05692 v2 pith:3TT6DDOC submitted 2018-10-12 cs.CR cs.DS

Aloni Cohen , Kobbi Nissim This is my paper · ORCID
classification cs.CRcs.DS
keywords algorithmprogramreconstructionattackdatasetlinearqueriesaircloak
verification ladder T0 review T1 audit T2 compute T3 formal

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We briefly report on a successful linear program reconstruction attack performed on a production statistical queries system and using a real dataset. The attack was deployed in test environment in the course of the Aircloak Challenge bug bounty program and is based on the reconstruction algorithm of Dwork, McSherry, and Talwar. We empirically evaluate the effectiveness of the algorithm and a related algorithm by Dinur and Nissim with various dataset sizes, error rates, and numbers of queries in a Gaussian noise setting.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Blind Targeting: Personalization under Third-Party Privacy Constraints

    stat.ME 2025-07 conditional novelty 7.0 of 10

    A Bayesian-optimization-based 'strategic querying' method recovers nearly all non-privacy-preserving targeting value from limited, noisy aggregate queries in simulations and on Criteo data.

  2. DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics

    cs.CR 2025-04 conditional novelty 6.0 of 10

    DeSIA infers sensitive attributes from limited fixed aggregate statistics by first checking whether a value is uniquely forced by the counts, then using a shadow-model classifier, outperforming reconstruction baseline...

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