Biblio

Data outsourcing to cloud has been a common IT practice nowadays due to its significant benefits. Meanwhile, security and privacy concerns are critical obstacles to hinder the further adoption of cloud. Although data encryption can mitigate the problem, it reduces the functionality of query processing, e.g., disabling SQL queries. Several schemes have been proposed to enable one-dimensional query on encrypted data, but multi-dimensional range query has not been well addressed. In this paper, we propose a secure and scalable scheme that can support multi-dimensional range queries over encrypted data. The proposed scheme has three salient features: (1) Privacy: the server cannot learn the contents of queries and data records during query processing. (2) Efficiency: we utilize hierarchical cubes to encode multi-dimensional data records and construct a secure tree index on top of such encoding to achieve sublinear query time. (3) Verifiability: our scheme allows users to verify the correctness and completeness of the query results to address server's malicious behaviors. We perform formal security analysis and comprehensive experimental evaluations. The results on real datasets demonstrate that our scheme achieves practical performance while guaranteeing data privacy and result integrity.

Multi-Writer Searchable Encryption, also known as public-key encryption with keyword search(PEKS), serves a wide spectrum of data sharing applications. It allows users to search over encrypted data encrypted via different keys. However, most of the existing PEKS schemes are built on classic security assumptions, which are proven to be untenable to overcome the threats of quantum computers. To address the above problem, in this paper, we propose a lattice-based searchable encryption scheme from the learning with errors (LWE) hardness assumption. Specifically, we observe that the keys of each user in a basic scheme are composed of large-sized matrices and basis of the lattice. To reduce the complexity of key management, our scheme is designed to enable users to directly use their identity for data encryption. We present several optimization techniques for implementation to make our design nearly practical. For completeness, we conduct rigorous security, complexity, and parameter analysis on our scheme, and perform comprehensive evaluations at a commodity machine. With a scenario of 100 users, the cost of key generation for each user is 125s, and the cost of searching a document with 1000 keywords is 13.4ms.

Symmetric Searchable Encryption (SSE) has received wide attention due to its practical application in searching on encrypted data. Beyond search, data addition and deletion are also supported in dynamic SSE schemes. Unfortunately, these update operations leak some information of updated data. To address this issue, forward-secure SSE is actively explored to protect the relations of newly updated data and previously searched keywords. On the contrary, little work has been done in backward security, which enforces that search should not reveal information of deleted data. In this paper, we propose the first practical and non-interactive backward-secure SSE scheme. In particular, we introduce a new form of symmetric encryption, named symmetric puncturable encryption (SPE), and construct a generic primitive from simple cryptographic tools. Based on this primitive, we then present a backward-secure SSE scheme that can revoke a server's searching ability on deleted data. We instantiate our scheme with a practical puncturable pseudorandom function and implement it on a large dataset. The experimental results demonstrate its efficiency and scalability. Compared to the state-of-the-art, our scheme achieves a speedup of almost 50x in search latency, and a saving of 62% in server storage consumption.

Modern distributed key-value stores are offering superior performance, incremental scalability, and fine availability for data-intensive computing and cloud-based applications. Among those distributed data stores, the designs that ensure the confidentiality of sensitive data, however, have not been fully explored yet. In this paper, we focus on designing and implementing an encrypted, distributed, and searchable key-value store. It achieves strong protection on data privacy while preserving all the above prominent features of plaintext systems. We first design a secure data partition algorithm that distributes encrypted data evenly across a cluster of nodes. Based on this algorithm, we propose a secure transformation layer that supports multiple data models in a privacy-preserving way, and implement two basic APIs for the proposed encrypted key-value store. To enable secure search queries for secondary attributes of data, we leverage searchable symmetric encryption to design the encrypted secondary indexes which consider security, efficiency, and data locality simultaneously, and further enable secure query processing in parallel. For completeness, we present formal security analysis to demonstrate the strong security strength of the proposed designs. We implement the system prototype and deploy it to a cluster at Microsoft Azure. Comprehensive performance evaluation is conducted in terms of Put/Get throughput, Put/Get latency under different workloads, system scaling cost, and secure query performance. The comparison with Redis shows that our prototype can function in a practical manner.