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Code and data for Linear Program Reconstruction in Practice

Aloni Cohen

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3608315", 
  "container_title": "Journal of Privacy and Confidentiality", 
  "title": "Code and data for Linear Program Reconstruction in Practice", 
  "issued": {
    "date-parts": [
      [
        2020, 
        1, 
        14
      ]
    ]
  }, 
  "abstract": "<p>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.</p>\n\n<p>The files in ./data were generated using the banking.psql database available at <a href=\"https://download.aircloak.com/\">https://download.aircloak.com/</a> (last retrieved 12 Dec 2018). The best places to get started ./code/attack_script.m and ./code/simulated_experiment_script.m.</p>", 
  "author": [
    {
      "family": "Aloni Cohen"
    }
  ], 
  "volume": "10", 
  "note": "NSF Graduate Research Fellowship, Facebook Fellowship, NSF Project CNS-1413920", 
  "version": "v20200114", 
  "type": "article", 
  "issue": "1", 
  "id": "3608315"
}
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Publication date:
January 14, 2020
DOI:
10.5281/zenodo.3608315

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Published in:
Journal of Privacy and Confidentiality: 10 (1).
Grants:
National Science Foundation:
  • TWC: TTP Option: Frontier: Collaborative: MACS: A Modular Approach to Cloud Security (1413920)
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MIT License

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Version v20200114 10.5281/zenodo.3608315 Jan 14, 2020
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