dc.contributor.author | Sharma, Aakash | |
dc.contributor.author | Nilsen, Thomas Bye | |
dc.contributor.author | Czerwinska, Katja P | |
dc.contributor.author | Onitiu, Daria | |
dc.contributor.author | Brenna, Lars | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | Johansen, Håvard D. | |
dc.date.accessioned | 2021-07-07T11:44:45Z | |
dc.date.available | 2021-07-07T11:44:45Z | |
dc.date.issued | 2021-05-13 | |
dc.description.abstract | Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using gossips. We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance. | en_US |
dc.identifier.citation | Sharma A, Nilsen, Czerwinska KP, Onitiu, Brenna L, Johansen D, Johansen HJ. Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies. Frontiers in Big Data. 2021;4 | en_US |
dc.identifier.cristinID | FRIDAID 1917323 | |
dc.identifier.doi | 10.3389/fdata.2021.624424 | |
dc.identifier.issn | 2624-909X | |
dc.identifier.uri | https://hdl.handle.net/10037/21816 | |
dc.language.iso | eng | en_US |
dc.publisher | Frontiers Media | en_US |
dc.relation.journal | Frontiers in Big Data | |
dc.relation.projectID | info:eu-repo/grantAgreement/RCN/IKTPLUSS-IKT/263248/Norway/Protecting Shared Data with Privacy Automatons// | en_US |
dc.relation.projectID | info:eu-repo/grantAgreement/RCN/IKTPLUSS-IKT/ 275516/Norway/Secure, Usable and Robust Cryptographic Voting Systems// | en_US |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2021 The Author(s) | en_US |
dc.subject | VDP::Mathematics and natural science: 400::Information and communication science: 420 | en_US |
dc.subject | VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420 | en_US |
dc.title | Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |