SAGMA: Secure Aggregation Grouped by Multiple Attributes

Timon Hackenjos, Florian Hahn, Florian Kerschbaum

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Encryption can protect data in outsourced databases - in the cloud - while still enabling query processing over the encrypted data. However, the processing leaks information about the data specific to the type of query, e.g., aggregation queries. Aggregation over user-defined groups using SQL's GROUP BY clause is extensively used in data analytics, e.g., to calculate the total number of visitors each month or the average salary in each department. The information leaked, e.g., the access pattern to a group, may reveal the group's frequency enabling simple, yet detrimental leakage-abuse attacks. In this work we present SAGMA - an encryption scheme for performing secure aggregation grouped by multiple attributes. The querier can choose any combination of one or multiple attributes in the GROUP BY clause among the set of all grouping attributes. The encryption scheme only stores semantically secure ciphertexts at the cloud and query processing hides the access pattern, i.e., the frequency of each group. We implemented our scheme and our evaluation results underpin its practical feasibility.
Original languageEnglish
Title of host publicationSIGMOD'20
Subtitle of host publicationProceedings of the 2020 ACM SIGMOD International Conference on Management of Data, June 2020
EditorsDavid Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, Hung Q. Ngo
PublisherACM Publishing
Pages587–601
ISBN (Electronic)978-1-4503-6735-6
DOIs
Publication statusPublished - Jun 2020

Fingerprint Dive into the research topics of 'SAGMA: Secure Aggregation Grouped by Multiple Attributes'. Together they form a unique fingerprint.

Cite this