A Distributed Framework for Scalable Search over Encrypted Documents

TL;DR

This paper presents a scalable, secure distributed search framework for encrypted documents in cloud environments, addressing big data challenges with efficient indexing, authorization, and update mechanisms.

Contribution

It introduces a provably secure distributed index with parallel retrieval, integrated authorization, and efficient update methods for encrypted data search.

Findings

The proposed index scales effectively to big data environments.

The search scheme limits information leakage in multi-user settings.

Empirical results demonstrate the efficiency of the framework on real datasets.

Abstract

Nowadays, huge amount of documents are increasingly transferred to the remote servers due to the appealing features of cloud computing. On the other hand, privacy and security of the sensitive information in untrusted cloud environment is a big concern. To alleviate such concerns, encryption of sensitive data before its transfer to the cloud has become an important risk mitigation option. Encrypted storage provides protection at the expense of a significant increase in the data management complexity. For effective management, it is critical to provide efficient selective document retrieval capability on the encrypted collection. In fact, considerable amount of searchable symmetric encryption schemes have been designed in the literature to achieve this task. However, with the emergence of big data everywhere, available approaches are insufficient to address some crucial real-world problems such as scalability. In this study, we focus on practical aspects of a secure keyword search mechanism over encrypted data on a real cloud infrastructure. First, we propose a provably secure distributed index along with a parallelizable retrieval technique that can easily scale to big data. Second, we integrate authorization into the search scheme to limit the information leakage in multi-user setting where users are allowed to access only particular documents. Third, we offer efficient updates on the distributed secure index. In addition, we conduct extensive empirical analysis on a real dataset to illustrate the efficiency of the proposed practical techniques.