Privacy-preserving data integration (PPDI) is a secure method to integrate datasets from different data sources while protecting the privacy of data. Existing PPDI work usually uses the outsourced framework and executes data integration through a cloud server. Due to the need to protect the privacy of the relations between IDs and associated data, the associated data must be encrypted or blinded before uploading to the cloud server, which leads to poor performance. For the efficient PPDI solution, we first carefully analyze the privacy goals of PPDI. After that, we adopt the distributed computing model, and then propose a multi-party PPDI protocol named DPPDI. Our scheme removes the overhead caused by encrypting associated data while protecting privacy, and realizes the outer join functionality and arbitrary combination of data sources. Besides, to avoid dropping records when duplicate IDs exist, we propose a method embedded into the PPDI protocol to handle duplicate IDs. Finally, we conduct extensive experiments to evaluate our scheme's performance, and the result shows that our scheme outperforms previous PPDI schemes.

Searchable symmetric encryption (SSE) is a technique that enables secure outsourcing of data to an untrusted cloud server without sacrificing search functionality. Recently, multi-user SSE schemes for data sharing, which support access control from various users, have gained attention. However, the access control mechanisms in existing schemes are not adequate for realistic data-sharing scenarios as they do not consider time constraints or only partially address them, making these mechanisms unsuitable for SSE schemes. To address this issue, we first highlight the importance of time constraints in multi-user SSE and propose a completely time-constrained SSE scheme under a two-server model. By taking advantage of the Lagrange interpolation and pre-computation, our proposed scheme enables searching over time-related encrypted data with owner-enforced time constraints. Additionally, we employ the blinding technique with the assistance of a semi-honest time server to ensure the completeness of time constraints, which is not guaranteed in existing works. Based on the leakage function, we prove the security of our proposed scheme in the simulation-based security model. Furthermore, extensive experiments demonstrate the practicality of our scheme in supporting time-constrained functions.

Building encrypted databases has been a long-standing challenge in the field of database security. In recent years, Structured Encryption (STE) has emerged as a promising approach to constructing encrypted databases, striking a balance between security and efficiency. Although existing STE-based encrypted database systems achieve high efficiency in query processing, all these schemes struggle to support rich queries with minimal information leakage. In this paper, we present a new STE-based encrypted database system, named Filter-integrated Encrypted Database (FinEDB), which supports exact-match and range queries, conjunctive queries and join operations, while maintaining limited information leakage. We first design a novel secure inverted index to avoid storage overhead blow-up when extending to support rich query capabilities. Then, we integrate Binary Fuse filters into our proposed inverted index to enable efficient query processing. By leveraging the homomorphic property of Binary Fuse filters, our approach leaks less information than existing STE-based solutions. Besides, we provide rigorous proof for our proposed scheme under the simulation paradigm. To evaluate the performance, we implement the prototype of FinEDB and compare it with the baseline STE-based scheme. Experiment results demonstrate that FinEDB is practical and can support rich queries on real-world databases.

After over a decade of intensive research, wireless sensing technology is nearing commercialization. However, the inherent openness of the wireless medium exposes this technology to security flaws and vulnerabilities. In this paper, we introduce RISiren to reveal the risk. RISiren is a pioneering end-to-end black-box attack system leveraging programmable metasurface with a high level of stealthiness. The key insight of RISiren lies in its ability to generate malicious multipath using metasurface, thereby disrupting wireless channel metrics influenced by genuine human activities and facilitating malicious attacks. To ensure the effectiveness of RISiren, we propose a novel metasurface configuration strategy aiming at creating human-like activities that stem from a comprehensive analysis of how human activities impact wireless signal propagation. We have implemented and validated RISiren using commercial Wi-Fi devices. Our evaluation involved testing our attack strategies against five state-of-the-art systems (including five different types of recognition frameworks) representative of the current landscape. The experimental results show that the adversarial wireless signals generated by RISiren achieve over 90% attack success rate on average, and remain robust and effective across different environments and deployment setups, including through wall attack scenarios.

Searchable Symmetric Encryption (SSE) is a valuable cryptographic tool that allows a client to retrieve its outsourced data from an untrusted server via keyword search. Initially, SSE research primarily focused on the efficiency-security trade-off. However, in recent years, attention has shifted towards range queries instead of exact keyword searches, resulting in significant developments in the SSE field. Despite the advancements in SSE schemes supporting range queries, many are susceptible to leakage-abuse attacks due to volumetric profile leakage. Although several schemes exist to prevent volume leakage, these solutions prove inefficient when dealing with large-scale datasets. In this paper, we highlight the efficiency-security trade-off for range queries in SSE. Subsequently, we propose a volume-hiding range SSE scheme that ensures efficient operations on extensive datasets. Leveraging the order-weighted inverted index and bitmap structure, our scheme achieves high search efficiency while maintaining the confidentiality of the volumetric profile. To facilitate searching within large-scale datasets, we introduce a partitioning strategy that divides a broad range into disjoint partitions and stores the information in a local binary tree. Through an analysis of the leakage function, we demonstrate the security of our proposed scheme within the ideal/real model simulation paradigm. Our experimental results further validate the practicality of our scheme with real-life large-scale datasets.

Through Searchable Symmetric Encryption (SSE), a user can make search over encrypted documents that are stored on an untrusted cloud server. Multi-client SSE schemes require that one client can search documents contributed by other clients and upload documents. Nevertheless, existing multi- client SSE schemes implement the fine-grained access control with high complexity. Although fine-grained access control adapts to complex scenarios, it is not necessary anytime and may cause heavy costs over computation in SSE schemes. Moreover, it is crucial to support documents updating and forward privacy. To combat that, we design a multi-client SSE scheme with efficient access control over dynamic encrypted documents. Specifically, we first modify Symmetric Hidden Vector Encryption (SHVE) and utilize Bloom filter to implement the access control, which reduces much of computation overhead. We then employ Oblivious Dynamic Cross-Tag (ODXT) protocol to preserve the forward privacy of our scheme. Finally, the corresponding security and experimental evaluation demonstrate both security and practicality of our scheme, respectively.

Low-power keywords recognition has been a focus of acoustic signal processing for several decades. This work investigates the domain-specific speech recognition microprocessor based on optimized one-dimensional convolutional recurrent neural network (1D-CRNN). Compared to previous DNN based frameworks, the proposed 1D-CRNN can process both the feature extraction and keywords classification, and achieve high recognition accuracy with reduced computation operations under wide range background noise SNRs. An energy-efficient 1D-CRNN accelerator is implemented to dynamically reconfigure and process the different layers. This accelerator has the characteristics of “More is Less” in three aspects: 1) the hybrid network with more complex layers is much more compact and requires less computation; 2) although the weight width quantized to 8 bits requires more memory size and multiplication energy cost, the required network neurons can be reduced and hardware utilization can be improved; 3) an energy-aware self-compensation tensor multiplication unit with dual power supply based on approximation design method can be utilized for 1D-CRNN computing. Compared to the state-of-the-art architectures, the novel more-is-less architecture can achieve a much lower power consumption of$1.4~\mu \text{W}\sim 2.1~\mu \text{W}$(over 80% reduced) under an industry 22nm technology, while maintaining higher system adaptability (support SNRs: −5dB~Clean) for 1~5 real-time keywords recognition.

Due to the combination of flexibility and efficiency, coarse-grained reconfigurable architectures (CGRAs) are suitable for the implementation of computing-intensive applications. However, with the growing performance requirements, the scale of CGRA increases exponentially, which leads to configuration performance degradation and configuration power rise. Based on the analysis of configuration context features, we optimize the context management scheme of CGRA from the aspects of context cache structure and replacement strategy. The context cache is structured hierarchically to reduce the memory overhead without configuration performance degradation and a hybrid context replacement algorithm is proposed to further increase the configuration efficiency with a novel context frequency weight factor. Experimental results show that the proposed context management scheme improves the configuration performance of the base CGRA significantly by 13.6%-20.5% for H.264 decoding and 13.6%-20.5% for MPEG2 decoding with only 43% context cache cost. Compared with other works, the proposed context management scheme shows the advantages of 2.3-6× less normalized context cache size and 2.3-2.7× cache efficiency.