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Security and Privacy Preservation of Evidence in Cloud Accountability Audits

Cloud accountability audits are promising to strengthen trust in cloud computing by providing reassurance about the processing data in the cloud according to data handling and privacy policies. To effectively automate cloud accountability audits, various distributed evidence sources need to be considered during evaluation. The types of information range from authentication and data access logging to location information, information on security controls and incident detection. Securing that information quickly becomes a challenge in the system design, when the evidence that is needed for the audit is deemed sensitive or confidential information. This means that securing the evidence at-rest as well as in-transit is of utmost importance. In this paper, we present a system that is based on distributed software agents which enables secure evidence collection with the purpose of automated evaluation during cloud accountability audits. We thereby present the integration of Insynd as a suitable cryptographic mechanism for securing evidence. We present our reasoning for choosing Insynd by showing a comparison of Insynd properties with requirements imposed by accountability evidence collection as well as an analysis how security threats are being mitigated by Insynd. We put special emphasis on security and privacy protection in our system analysis.

Thomas Rübsamen, Tobias Pulls, Christoph Reich
Publication Date: 
Wednesday, February 3, 2016
Lisbon, Portugal
Publication Reference: 

Thomas Rübsamen, Tobias Pulls, Christoph Reich, Simone Fischer-Hübner, "Security and Privacy Preservation of Evidence in Cloud Accountability Audits", Cloud Computing and Services Science, 5th International Conference, CLOSER 2015, Lisbon, Portugal, May 20-22, 2015, Revised Selected Papers, Volume 581 of the series Communications in Computer and Information Science, Pages 95-114, Print ISBN 978-3-319-29581-7, Online ISBN 978-3-319-29582-4, Springer International Publishing, DOI: 10.1007/978-3-319-29582-4_6.