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dc.contributor.authorSchwarzkopf, Malte
dc.contributor.authorKohler, Eddie
dc.contributor.authorKaashoek, M. Frans
dc.contributor.authorMorris, Robert Tappan
dc.date.accessioned2022-07-19T16:10:31Z
dc.date.available2021-09-20T18:21:47Z
dc.date.available2022-07-19T16:10:31Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/132311.2
dc.description.abstract© 2019, Springer Nature Switzerland AG. New laws such as the European Union’s General Data Protection Regulation (GDPR) grant users unprecedented control over personal data stored and processed by businesses. Compliance can require expensive manual labor or retrofitting of existing systems, e.g., to handle data retrieval and removal requests. We argue for treating these new requirements as an opportunity for new system designs. These designs should make data ownership a first-class concern and achieve compliance with privacy legislation by construction. A compliant-by-construction system could build a shared database, with similar performance as current systems, from personal databases that let users contribute, audit, retrieve, and remove their personal data through easy-to-understand APIs. Realizing compliant-by-construction systems requires new cross-cutting abstractions that make data dependencies explicit and that augment classic data processing pipelines with ownership information. We suggest what such abstractions might look like, and highlight existing technologies that we believe make compliant-by-construction systems feasible today. We believe that progress towards such systems is at hand, and highlight challenges for researchers to address to make them a reality.en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-030-33752-0_3en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titlePosition: GDPR Compliance by Constructionen_US
dc.typeArticleen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-22T14:22:58Z
dspace.orderedauthorsSchwarzkopf, M; Kohler, E; Frans Kaashoek, M; Morris, Ren_US
dspace.date.submission2020-12-22T14:23:02Z
mit.journal.volume11721en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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