Adversarial contract design for private data commercialization
The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem as a mechanism design problem, specifically using a contract-theoretic approach. Our propo...
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sg-smu-ink.sis_research-58002020-01-16T10:11:15Z Adversarial contract design for private data commercialization NAGHIZADEH, Parinaz SINHA, Arunesh The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem as a mechanism design problem, specifically using a contract-theoretic approach. Our proposed adversarial contract design framework provides a fundamental extension to the classic contract theory set-up in order to account for the heterogeneity in honest buyers’ demands for data, as well as the presence of adversarial buyers who may purchase data to compromise its privacy. We propose the notion of Price of Adversary (PoAdv) to quantify the effects of adversarial users on the data seller’s revenue, and provide bounds on the PoAdv for various classes of adversary utility. We also provide a fast approximate technique to compute contracts in the presence of adversaries. 2019-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4797 info:doi/10.1145/3328526.3329633 https://ink.library.smu.edu.sg/context/sis_research/article/5800/viewcontent/ec305_naghizadehA_1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University contract theory pricing private data data commercialization Data Storage Systems |
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contract theory pricing private data data commercialization Data Storage Systems NAGHIZADEH, Parinaz SINHA, Arunesh Adversarial contract design for private data commercialization |
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The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem as a mechanism design problem, specifically using a contract-theoretic approach. Our proposed adversarial contract design framework provides a fundamental extension to the classic contract theory set-up in order to account for the heterogeneity in honest buyers’ demands for data, as well as the presence of adversarial buyers who may purchase data to compromise its privacy. We propose the notion of Price of Adversary (PoAdv) to quantify the effects of adversarial users on the data seller’s revenue, and provide bounds on the PoAdv for various classes of adversary utility. We also provide a fast approximate technique to compute contracts in the presence of adversaries. |
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NAGHIZADEH, Parinaz SINHA, Arunesh |
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NAGHIZADEH, Parinaz SINHA, Arunesh |
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NAGHIZADEH, Parinaz |
title |
Adversarial contract design for private data commercialization |
title_short |
Adversarial contract design for private data commercialization |
title_full |
Adversarial contract design for private data commercialization |
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Adversarial contract design for private data commercialization |
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Adversarial contract design for private data commercialization |
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adversarial contract design for private data commercialization |
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Institutional Knowledge at Singapore Management University |
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2019 |
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https://ink.library.smu.edu.sg/sis_research/4797 https://ink.library.smu.edu.sg/context/sis_research/article/5800/viewcontent/ec305_naghizadehA_1_.pdf |
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