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. CCS Concepts: • Security and privacy → Domain-specific security and privacy architectures; • Computing methodologies → Distributed programming languages.

Object Graph Programming

“…Although powerful, LINQ provides no support to query arbitrary objects and their relations. Language integrated queries have seen renewed interest [22,54,57,61,72,77,78,94,96,102] since the release of the LINQ framework. Similar frameworks for Java include SBQL4J [107] and Quaere [75].…”

No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems

“…To date, the operator placement algorithms focus only on utility-related metrics, like latency and throughput, to decide which SPS nodes will process the queries [15,16]. There exists limited work that considers privacy to inform the operator placement, e.g., [23] combines privacy and utility goals in the placement decisions, but only from the viewpoint of designing a query language for SPSs, while [6] incorporates user's trust into an SPS node to the cost model, however assessing trust of resources in a multilayered SPS, whose properties are hidden from end users, is infeasible. In contrast, Prinseps approaches the task of operator placement from the point of balancing a PUT.…”

An authorization model for query execution in the cloud

“…Work closest to ours has addressed the problem of protecting data confidentiality in distributed computations (e.g., [11,22,27,32]). In [32] the authors present an approach to collaboratively execute queries on data subject to access restrictions, considering different join evaluation strategies.…”

“…In [32] the authors present an approach to collaboratively execute queries on data subject to access restrictions, considering different join evaluation strategies. In [27] the authors propose an operator placement approach aimed at satisfying privacy constraints, while maximizing performance in query evaluation. The proposed solution relies on programming language techniques for regulating and controlling information flows.…”