Authors:
Axel Michel
1
;
Benjamin Nguyen
1
and
Philippe Pucheral
2
Affiliations:
1
INSA-CVL, Petrus team and Inria Saclay & UVSQ, France
;
2
Petrus team and Inria Saclay & UVSQ, France
Keyword(s):
Data Privacy and Security, Big Data, Distributed Query Processing, Secure Hardware.
Related
Ontology
Subjects/Areas/Topics:
Data and Application Security and Privacy
;
Data Engineering
;
Data Privacy and Security
;
Database Architecture and Performance
;
Databases and Data Security
;
Information and Systems Security
Abstract:
The benefit of performing Big data computations over individual’s microdata is manifold, in the medical,
energy or transportation fields to cite only a few, and this interest is growing with the emergence of smart
disclosure initiatives around the world. However, these computations often expose microdata to privacy leakages,
explaining the reluctance of individuals to participate in studies despite the privacy guarantees promised
by statistical institutes. This paper proposes a novel approach to push personalized privacy guarantees in the
processing of database queries so that individuals can disclose different amounts of information (i.e. data at different
levels of accuracy) depending on their own perception of the risk. Moreover, we propose a decentralized
computing infrastructure based on secure hardware enforcing these personalized privacy guarantees all along
the query execution process. A performance analysis conducted on a real platform shows the effectiveness of
the approach.