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 a scalable, private, and searchable key-value store. We first design a secure data partition algorithm that distributes encrypted data evenly across a cluster of nodes. Based on this algorithm, we then implement an encrypted key-value store. To enable secure search queries for given attributes or keywords, we leverage searchable symmetric encryption to design the encrypted local indexes that consider security, efficiency, and data locality simultaneously. Performance evaluation at Microsoft Azure is conducted in terms of Put/Get throughput and latency under different workloads. The comparison with HBase shows that our prototype can function in a practical manner.