Recent progress in adversarial attacks on 3D point clouds, particularly in achieving spatial imperceptibility and high attack performance, presents significant challenges for defenders. Current defensive approaches remain cumbersome, often requiring invasive model modifications, expensive training procedures or auxiliary data access. To address these threats, in this paper, we propose a plug-and-play and non-invasive defense mechanism in the spectral domain, grounded in a theoretical and empirical analysis of the relationship between imperceptible perturbations and high-frequency spectral components. Building upon these insights, we introduce a novel purification framework, termed PWAVEP, which begins by computing a spectral graph wavelet domain saliency score and local sparsity score for each point. Guided by these values, PWAVEP adopts a hierarchical strategy, it eliminates the most salient points, which are identified as hardly recoverable adversarial outliers. Simultaneously, it applies a spectral filtering process to a broader set of moderately salient points. This process leverages a graph wavelet transform to attenuate high-frequency coefficients associated with the targeted points, thereby effectively suppressing adversarial noise. Extensive evaluations demonstrate that the proposed PWAVEP achieves superior accuracy and robustness compared to existing approaches, advancing the state-of-the-art in 3D point cloud purification. Code and datasets are available at https://github.com/a772316182/pwavep

Outsourcing logistic regression classification services to the cloud is highly beneficial for streaming data. However, it raises critical privacy concerns for the input data and the training models. Current solutions for encrypted logistic regression classification fall short in the processing of encrypted streaming data. In this paper, we propose a privacy-preserving logistic regression model (PrivESD), which allows computation over encrypted streaming data. First, we propose a lightweight framework, which creates a collaborative workload between the cloud and the edge thereby reducing the computation complexity of the cloud and the number of communications between the data owners and the cloud. Second, we develop a tailored building block library with strong data confidentiality that supports comparison operations. This library has then been used to construct logistic regression. Finally, we devise a processing scheme that uses stochastic gradient descent with momentum to train the model to prevent the problem of local optimal convergence with streaming data. We have conducted extensive performance analysis demonstrating that the proposed protocols such as our secure compare protocol outperforms existing schemes such as Bost [44] and Guo [45], and that the encryption and decryption operations of PrivESD are similar to that of Paillier [42] with$2^{30}$keys.

Cloud computing has become a dominant paradigm for outsourcing computationally intensive tasks. However, the lack of transparency in remote execution poses significant challenges to integrity and trust. Verifiable computation (VC) mitigates these issues by enabling clients to efficiently verify outsourced results. Existing VC schemes often incur high verification costs, scale poorly, and exhibit low computational efficiency. We propose the Parallel and Verifiable Polynomial Computation (PVPC) scheme, designed for secure, privacy-preserving, and scalable polynomial evaluation in heterogeneous cloud-edge environments. PVPC reformulates polynomial evaluation as matrix-vector operations combined with random sparse blinding, enabling parallel execution across multiple sub-servers while preserving input privacy. It achieves sublinear complexity in the polynomial degree through row-wise factorization and enables aggregated batch verification via bilinear pairings, thereby reducing per-task verification overhead. We present formal correctness and security proofs under the Computational Diffie-Hellman assumption, and evaluate PVPC against state-of-the-art VC protocols. Experimental results show that PVPC substantially outperforms existing schemes in computation, verification, and recovery phases-particularly for high-degree polynomials-while maintaining practical communication costs. These features make PVPC well-suited for large-scale, latency-sensitive, and distributed verifiable computing applications.

With development of online social networks, many criminal suspects use social network to communicate with each other. In order to obtain valuable criminal clues, considerable research works have been done to analyze criminal suspects' social data. However, most of them did not pay much attention on privacy-preserving problems, which may leak some sensitive data in the analysis process. To solve this problem, we propose a novel analysis approach of criminal suspects by exploiting social data and crime data that are collected by social network and police information systems. We enable the social cloud server and public security cloud server to exchange social information of criminal suspects and user's public information in a privacy-preserving way. Specifically, we propose a privacy-preserving data retrieving method based on oblivious transfer to guarantee that only the authorized entities can perform queries on suspects' social data, while the social cloud server cannot infer anything during the query. Moreover, several building blocks, such as encrypted data comparing, secure classification and regression tree (CART) model are also proposed. Based on these building blocks, we designed a privacy-preserving criminal suspects sensing scheme. Finally, we demonstrate a performance evaluation which shows that our scheme can enhance analysis of criminal suspects without privacy leakage, while with low overhead.