Privacy-Preserving Analytics for Data Markets Using MPC

Karl Koch; Stephan Krenn; Donato Pellegrino; Sebastian Ramacher

doi:10.1007/978-3-030-72465-8_13

Privacy-Preserving Analytics for Data Markets Using MPC

Karl Koch
, Stephan Krenn
, Donato Pellegrino
, Sebastian Ramacher

Graz University of Technology
TX - Tomorrow Explored

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

Abstract

Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union’s General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capabl e of performing privacy-preserving computations on the data. Besides specifying the architecture of such a data market, we also present a privacy-risk analysis of the market following the LINDDUN methodology.

Original language	English
Title of host publication	Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	226-246
Number of pages	21
ISBN (Print)	978-3-030-72464-1
DOIs	https://doi.org/10.1007/978-3-030-72465-8_13
Publication status	Published - 2021
Event	IFIP Summer School on Privacy and Identity Management - Duration: 20 Sept 2020 → 23 Sept 2020

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	619 IFIP

Conference

Conference	IFIP Summer School on Privacy and Identity Management
Period	20/09/20 → 23/09/20

Research Field

Cyber Security

Keywords

Data market
Multi-party computation
Privacy analysis

Access to Document

10.1007/978-3-030-72465-8_13

Cite this

Koch, K., Krenn, S., Pellegrino, D., & Ramacher, S. (2021). Privacy-Preserving Analytics for Data Markets Using MPC. In Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology (pp. 226-246). (IFIP Advances in Information and Communication Technology; Vol. 619 IFIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72465-8_13

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abstract = "Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union{\textquoteright}s General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capabl e of performing privacy-preserving computations on the data. Besides specifying the architecture of such a data market, we also present a privacy-risk analysis of the market following the LINDDUN methodology.",

keywords = "Data market, Multi-party computation, Privacy analysis",

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note = "IFIP Summer School on Privacy and Identity Management ; Conference date: 20-09-2020 Through 23-09-2020",

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Koch, K, Krenn, S, Pellegrino, D & Ramacher, S 2021, Privacy-Preserving Analytics for Data Markets Using MPC. in Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology. IFIP Advances in Information and Communication Technology, vol. 619 IFIP, Springer Science and Business Media Deutschland GmbH, pp. 226-246, IFIP Summer School on Privacy and Identity Management, 20/09/20. https://doi.org/10.1007/978-3-030-72465-8_13

Privacy-Preserving Analytics for Data Markets Using MPC. / Koch, Karl; Krenn, Stephan; Pellegrino, Donato et al.
Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology. Springer Science and Business Media Deutschland GmbH, 2021. p. 226-246 (IFIP Advances in Information and Communication Technology; Vol. 619 IFIP).

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

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AB - Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union’s General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capabl e of performing privacy-preserving computations on the data. Besides specifying the architecture of such a data market, we also present a privacy-risk analysis of the market following the LINDDUN methodology.

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KW - Privacy analysis

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Koch K, Krenn S, Pellegrino D, Ramacher S. Privacy-Preserving Analytics for Data Markets Using MPC. In Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology. Springer Science and Business Media Deutschland GmbH. 2021. p. 226-246. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-72465-8_13

Privacy-Preserving Analytics for Data Markets Using MPC

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