Title
Language-integrated privacy-aware distributed queries
Abstract
Distributed query processing is an effective means for processing large amounts of data. To abstract from the technicalities of distributed systems, algorithms for operator placement automatically distribute sequential data queries over the available processing units. However, current algorithms for operator placement focus on performance and ignore privacy concerns that arise when handling sensitive data. We present a new methodology for privacy-aware operator placement that both prevents leakage of sensitive information and improves performance. Crucially, our approach is based on an information-flow type system for data queries to reason about the sensitivity of query subcomputations. Our solution unfolds in two phases. First, placement space reduction generates deployment candidates based on privacy constraints using a syntax-directed transformation driven by the information-flow type system. Second, constraint solving selects the best placement among the candidates based on a cost model that maximizes performance. We verify that our algorithm preserves the sequential behavior of queries and prevents leakage of sensitive data. We implemented the type system and placement algorithm for a new query language SecQL and demonstrate significant performance improvements in benchmarks.
Year
DOI
Venue
2019
10.1145/3360593
Proceedings of the ACM on Programming Languages
Keywords
Field
DocType
Data Privacy, Information-Flow Type System, Operator Placement, SQL, Scala
World Wide Web,Computer science
Journal
Volume
Issue
Citations 
3
OOPSLA
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Guido Salvaneschi135434.50
Mirko Köhler271.13
Daniel Sokolowski301.35
Philipp Haller444127.11
Sebastian Erdweg546133.21
Mira Mezini63171211.04