@inproceedings{73caf1543ace4cfc92473e34bc59c25a,
title = "Privacy-Preserving Analytics for Data Markets Using MPC",
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",
author = "Karl Koch and Stephan Krenn and Donato Pellegrino and Sebastian Ramacher",
year = "2021",
doi = "10.1007/978-3-030-72465-8\_13",
language = "English",
isbn = "9783030724641",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "226--246",
booktitle = "IFIP Advances in Information and Communication Technology",
address = "Germany",
note = "IFIP Summer School on Privacy and Identity Management 2021 ; Conference date: 17-08-2021",
}