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Learning to hash is of particular interest in information retrieval for large-scale data due to its high efficiency and effectiveness. Most studies in hashing concentrate on constructing new hashing models, but rarely touch the correlation and redundancy between hash bits derived. In this article, we first introduce a general schema of hash bit reduction to derive compact and informative binary codes for hashing techniques. Further, we take locality sensitive hashing, one of the most widely-used hashing methods, as an example and propose a novel and two-stage binary code refinement method under the reduction schema. Specifically, the proposed method includes two stages, i.e., bit evaluation and bit refinement. The former stage aims to initially extract a small portion of informative hash bits in terms of their importance and quality evaluated by bit balance and similarity preservation. Then, the representation capabilities of the reduced hash bits are strengthened further by refining their binary values. The purpose of refinement is to lessen the correlations and redundancies between the reduced bits, making themselves more discriminative. The experimental results on three widely-used data collections confirm the effectiveness of the proposed bit reduction method and its superiority over the state-of-the-art hashing methods, as well as a bit selection method.

This letter considers a novel relay-assisted, multi-user multi-tag uplink symbiotic radio system, in which a relay node forwards multiple users’ signals to a base station (BS), meanwhile powering multiple backscatter tags. We put forth a new joint transmit and receive beamforming design problem to minimize the relay’s transmit power while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints of both the primary users and the tags. An alternating optimization algorithm based on semi-definite programming (SDP) and zero-forcing is proposed to solve this non-convex power minimization problem. The proposed approach is also extended to jointly optimize the transmit/receive beamforming to maximize the minimal SINR of the tags under a given relay power budget. Numerical results show that the proposed beamforming designs significantly outperform the benchmarks.

With the increasing awareness of personal health, personal health data management has become an important part of people's lives. Smart wearable devices (SWDs) collect people's personal health data, and then store the data on cloud. Authorized entities access the data to provide personalized health services. However, these personal health data contain a large amount of sensitive information, which may pose a significant threat to people's lives and property. To address this, this paper proposes a privacy-preserving solution. SWD data is encrypted, and secure indexes are created using Bloom filter and 0-1 encoding. Encrypted data and indexes are stored in a semi-trusted cloud. Only authorized entities can access the ciphertexts, ensuring secure personalized health management. Extensive experiments validate the scheme's efficiency in index construction, query token generation, and ciphertext search. Security analysis confirms no external entity, including the cloud, gains additional information during retrieval.

Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, we propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server, to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.