Spatial crowdsourcing can quickly assign tasks and obtain feedback based on task requirements and workers’ locations, which brings great convenience to task assignment. However, sensitive information can also be easily obtained by spatial crowdsourcing platforms. To prevent information leakage, various privacy-preserving task assignment schemes have been proposed. However, existing schemes have low query efficiency and may leak pattern privacy, task content, or worker preference. To address the above challenges, this paper proposes a differentiated privacy-preserving task assignment scheme based on generative adversarial networks in spatial crowdsourcing–DPGAN-SC. This scheme leverages generative adversarial networks to generate disguised locations for both tasks and workers, which are then used in the task-matching process. Within the standard area range, no location can be distinguished, ensuring location privacy while preventing adversaries from analyzing search patterns through matching results. The combination of location disguise and task content encryption makes it impossible for adversaries to infer worker preferences and access patterns through the matching process. In addition, to meet differentiated privacy requirements, DPGAN-SC leverages generative adversarial networks to design a three-level privacy-classification mechanism. This mechanism categorizes private data while minimizing unnecessary privacy overhead. Compared to existing schemes, DPGAN-SC improves query efficiency by 100 times while ensuring comprehensive privacy preservation.

With the rapid development of cloud computing, more and more users are storing sensitive data on cloud servers, making the privacy-preserving of data particularly important. Dynamic searchable symmetric encryption enables efficient retrieval of encrypted data in cloud computing environments while preserving data privacy. However, existing solutions are not effective in defending against various query-recovery attacks. Therefore, this paper focuses on the privacy-preserving of dynamic searchable symmetric encryption, and proposes a privacy-preserving dynamic searchable symmetric encryption based on anonymization and differential privacy – DADP. Firstly, the original indexes are synthesized into fake indexes using the anonymization hash technology. The synthetic indexes possess randomness and irreversibility, making it impossible for adversaries to infer the generation process of the synthetic indexes or recover the original indexes. Additionally, by using differential privacy to process composite indexes, the privacy of keywords and index information is protected, preventing adversaries from inferring sensitive information based on query results. This approach provides dual privacy-preserving. Compared to other schemes, our scheme achieves type-I backward privacy and can withstand seven types of query recovery attacks. And it improves update and query efficiency by 10-100 times.

With the extensive research and application on Face Recognition (FR) model in daily life, the security of FR has attracted much attention as it is easily attacked by adversarial examples. Specifically, adversarial attacks can cause the model to make completely erroneous judgments by making very subtle changes to the source image. Therefore, it is of great significance for studying adversarial attacks that can improve the robustness and security of FR models. However, most of the existing attacks have low transferability of attack and high vulnerability to denoising defense models. To solve the above problems, a multi-attribute semantic adversarial attack based on cross-layer interpolation(C&A Adv) is proposed, which can generate imperceptible adversarial images whith high success rate and robustness to denoising defense methods. Particularly, C&A Adv semantically edit images by cross-layer feature space interpolation, which not only generates high quality adversarial images, but also has the robustness to partial denoising defense methods. In addition, to improve the success rate of the attack, several attributes are selected to edit instead of just one. According to the marginal gain of each attribute calculated in different face images, several attributes with the greatest marginal gain are selected to edit. Comparison and verification on CelebA dataset show that the C&A Adv achieves good experimental result.

Because it allows users to browse encrypted documents on an untrusted cloud server, searchable symmetric encryption has gotten a great deal of interest. However, based on the leakage of access and search patterns, the cloud server can infer users’ private data. Despite the fact that researchers have presented a number of strategies for protecting access or search patterns, all of them have a substantial computational and communication overhead. To that aim, this paper utilizes differential privacy technology to provide an efficient pattern-protecting dynamic searchable symmetric encryption scheme (DF-DSSE). Specifically, differential privacy’s false positives and false negatives are used to obfuscate the documents associated with each keyword, protecting access patterns. In particular, to reduce the computational and communication overhead associated by obfuscating query results, a tag symmetric encryption primitive is provided to encrypt indexes and query tokens. Furthermore, since the DF-DSSE scheme stores the token tags using the Bid Compress compression structure and accesses the documents corresponding to each keyword independently, an adversary cannot obtain the number of keywords or the frequency with which they are accessed, achieving the goal of protecting search patterns. In comparison to previous schemes, the DF-DSSE scheme enhances update and query efficiency and security, according to the security analysis and simulation experiment results.

Sharing and searching encrypted data securely in outsourced environments poses a challenge due to possible cooperation between compromised users and untrusted servers. This paper studies the problem of multi-client dynamic searchable symmetric encryption, where a data owner stores encrypted documents on an untrusted remote server and selectively allows multiple users to access them through keyword search queries. The paper proposes a practical multi-client conjunctive searchable symmetric encryption scheme in a redactable blockchain to address this challenge. This scheme achieves multi-client sublinear conjunctive keyword search, and the data owner can authorize clients to access the documents. The scheme combines encryption primitives with novel access control techniques and constructs a redactable blockchain$\zeta$-oblivious group cross tags for sublinear search. The system's security is proven in a simulation-based security model. A prototype implementation using a blockchain-based approach is developed and evaluated on a real-world database containing millions of documents to demonstrate its practicality.

One of the major security challenges faced by wireless sensor networks(WSNs) is establishing a secure link for communication between neighboring sensor nodes. Finding a balance between connection, overhead, and resilience against node capture attacks is difficult due to the resource limits of sensor nodes. We propose a new three-dimensional key distribution scheme for wireless sensor networks based on polynomial and random key distribution schemes. The key pool is divided into two sections in the proposed scheme: key pool 1 is generated by the polynomial pool, and key pool 2 is generated by key pool 1. A three-dimensional key distribution model is constructed using the key pool and the coefficients of the polynomials. It can enhance network resilience while maintaining good connectivity by dynamically adjusting the degree of polynomials and the size of the polynomial pool. This paper analyzes the performance of the proposed scheme and compares it with other schemes. The results show that the proposed scheme has better local connectivity and resilience against node capture attacks when compared with the previous schemes.

Adapting detection to different shot types is a significant challenge for video summarization methods based on shot boundary detection. In our recent work, a new graph model was introduced in the feature modelling of frames and analysed for changes in graph structure to improve the detection of shot boundaries. In this paper, we further explore the potential of graph models and propose a more general framework for online, real-time automatic video summarization. The framework develops a novel adaptive multiview graph difference analysis method to improve the algorithm’s robustness in detecting different shot transitions. Previous fusion methods typically used a priori knowledge to assign weights to the various feature differences from videos. In contrast, our framework can weigh and fuse the resulting differences by learning the importance of various video features from the structural changes of the corresponding multiview graphs. Additionally, we propose a new threshold-based adaptive decision method which can dynamically select the most accurate shot boundary decision threshold by analysing a small number of historical frames and learning the tolerance factor in the current shot. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of precision and F-score on the VSUMM and YouTube datasets.