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2021-10-04
Wang, Xiangyu, Ma, Jianfeng, Liu, Ximeng, Deng, Robert H., Miao, Yinbin, Zhu, Dan, Ma, Zhuoran.  2020.  Search Me in the Dark: Privacy-preserving Boolean Range Query over Encrypted Spatial Data. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2253–2262.
With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.
2020-09-14
Ma, Zhuo, Liu, Yang, Liu, Ximeng, Ma, Jianfeng, Li, Feifei.  2019.  Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices. IEEE Internet of Things Journal. 6:8406–8420.
Most of the current intelligent Internet of Things (IoT) products take neural network-based speech recognition as the standard human-machine interaction interface. However, the traditional speech recognition frameworks for smart IoT devices always collect and transmit voice information in the form of plaintext, which may cause the disclosure of user privacy. Due to the wide utilization of speech features as biometric authentication, the privacy leakage can cause immeasurable losses to personal property and privacy. Therefore, in this paper, we propose an outsourced privacy-preserving speech recognition framework (OPSR) for smart IoT devices in the long short-term memory (LSTM) neural network and edge computing. In the framework, a series of additive secret sharing-based interactive protocols between two edge servers are designed to achieve lightweight outsourced computation. And based on the protocols, we implement the neural network training process of LSTM for intelligent IoT device voice control. Finally, combined with the universal composability theory and experiment results, we theoretically prove the correctness and security of our framework.
Wu, Pengfei, Deng, Robert, Shen, Qingni, Liu, Ximeng, Li, Qi, Wu, Zhonghai.  2019.  ObliComm: Towards Building an Efficient Oblivious Communication System. IEEE Transactions on Dependable and Secure Computing. :1–1.
Anonymous Communication (AC) hides traffic patterns and protects message metadata from being leaked during message transmission. Many practical AC systems have been proposed aiming to reduce communication latency and support a large number of users. However, how to design AC systems which possess strong security property and at the same time achieve optimal performance (i.e., the lowest latency or highest horizontal scalability) has been a challenging problem. In this paper, we propose an ObliComm framework, which consists of six modular AC subroutines. We also present a strong security definition for AC, named oblivious communication, encompassing confidentiality, unobservability, and a new requirement sending-and-receiving operation hiding. The AC subroutines in ObliComm allow for modular construction of oblivious communication systems in different network topologies. All constructed systems satisfy oblivious communication definition and can be provably secure in the universal composability (UC) framework. Additionally, we model the relationship between the network topology and communication measurements by queuing theory, which enables the system's efficiency can be optimized and estimated by quantitative analysis and calculation. Through theoretical analyses and empirical experiments, we demonstrate the efficiency of our scheme and soundness of the queuing model.
2020-07-24
Li, Qi, Ma, Jianfeng, Xiong, Jinbo, Zhang, Tao, Liu, Ximeng.  2013.  Fully Secure Decentralized Key-Policy Attribute-Based Encryption. 2013 5th International Conference on Intelligent Networking and Collaborative Systems. :220—225.

In previous multi-authority key-policy attribute-based Encryption (KP-ABE) schemes, either a super power central authority (CA) exists, or multiple attribute authorities (AAs) must collaborate in initializing the system. In addition, those schemes are proved security in the selective model. In this paper, we propose a new fully secure decentralized KP-ABE scheme, where no CA exists and there is no cooperation between any AAs. To become an AA, a participant needs to create and publish its public parameters. All the user's private keys will be linked with his unique global identifier (GID). The proposed scheme supports any monotonic access structure which can be expressed by a linear secret sharing scheme (LSSS). We prove the full security of our scheme in the standard model. Our scheme is also secure against at most F-1 AAs corruption, where F is the number of AAs in the system. The efficiency of our scheme is almost as well as that of the underlying fully secure single-authority KP-ABE system.