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.

Middleboxes are essential for a wide range of advanced traffic processing in modern enterprise networks. Recent trend of deploying middleboxes in cloud as virtualized services further expands potential benefits of middleboxes while avoiding local maintenance burdens. Despite promising, designing outsourced middleboxes still faces several security challenges. First, many middlebox processing services, like intrusion detection, require packet payload inspection, while the ever-increasing adoption of HTTPS limits the function due to the end-to-end encryption. Second, many packet inspection rules used by middleboxes can be proprietary in nature. They may contain sensitive information of enterprises, and thus need strong protection when configuring middleboxes in untrusted outsourced environments. In this paper, we propose a practical system architecture for outsourced middleboxes to perform deep packet inspection over encrypted traffic, without revealing either packet payloads or inspection rules. Our first design is an encrypted high-performance rule filter that takes randomized tokens from packet payloads for encrypted inspection. We then elaborate through carefully tailored techniques how to comprehensively support open-source real rulesets. We formally analyze the security strength. Implementations at Amazon Cloud show that our system introduces roughly 100 millisecond latency in each connection initialization, with individual processing throughput over 3500 packets/second for 500 concurrent connections.