2016 (English)In: Cryptology and Network Security: 15th International Conference, CANS 2016 Milan, Italy, November 14–16, 2016 Proceedings / [ed] Sara Foresti and Giuseppe Persiano, Springer, 2016, Vol. 10052, 661-669 p.Conference paper (Refereed)
When considering data provenance some problems arise from the need to safely handle provenance related functionality. If some modifications have to be performed in a data set due to provenance related requirements, e.g. remove data from a given user or source, this will affect not only the data itself but also all related models and aggregated information obtained from the data. This is specially aggravated when the data are protected using a privacy method (e.g. masking method), since modification in the data and the model can leak information originally protected by the privacy method. To be able to evaluate privacy related problems in data provenance we introduce the notion of integral privacy as compared to the well known definition of differential privacy
Place, publisher, year, edition, pages
Springer, 2016. Vol. 10052, 661-669 p.
Lecture Notes in Computer Science, ISSN 0302-9743
IdentifiersURN: urn:nbn:se:his:diva-13359DOI: 10.1007/978-3-319-48965-0_44ISI: 000389953600044ScopusID: 2-s2.0-84994813221ISBN: 978-3-319-48964-3 (print)ISBN: 978-3-319-48965-0 (electronic)OAI: oai:DiVA.org:his-13359DiVA: diva2:1071375
15th International Conference on Cryptology and Network Security, CANS 2016, Milan, Italy, November 14-16, 2016