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Distributed Privacy-Preserving Mean Estimation

Title data

Schönfeld, Mirco ; Werner, Martin:
Distributed Privacy-Preserving Mean Estimation.
In: Institute of Electrical and Electronics Engineers (ed.): 2014 International Conference on Privacy and Security in Mobile Systems (PRISMS). - Piscataway, NJ : IEEE , 2014 . - pp. 34-41
ISBN 978-1-4799-4627-3
DOI: https://doi.org/10.1109/PRISMS.2014.6970597

Abstract in another language

Due to the rise of mobile computing and smartphones, a lot of information about groups has become accessible. This information shall often be kept secret. Hence distributed algorithms for privacy-preserving distribution estimation are needed. Most research currently focuses on privacy in a database, where a single entity has collected the secret information and privacy is ensured between query results and the database. In fully distributed systems such as sensor networks it is often infeasible to move the data towards a central entity for processing. Instead, distributed algorithms are needed. With this paper we propose a fully distributed, privacy-friendly, consensus-based approach. In our approach all nodes cooperate to generate a sufficiently random obfuscation of their secret values until the estimated and obfuscated values of the individual nodes can be safely published. Then the calculations can be done on this replacement containing only non-secret values but recovering some aspects (mean, standard deviation) of the original distribution.

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Languages and Literature
Faculties > Faculty of Languages and Literature > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung
Faculties > Faculty of Languages and Literature > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung - Juniorprof. Dr. Mirco Schönfeld
Faculties
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 18 Nov 2021 13:37
Last Modified: 18 Nov 2021 13:37
URI: https://eref.uni-bayreuth.de/id/eprint/67897