Spatial crowdsourcing (SC) recruits online workers to perform geographically relevant tasks based on their real time locations, which exposes sensitive location information. Workers’ heterogeneous privacy needs make uniform protection ineffective and degrade service quality. To address these issues, this paper proposes a deep reinforcement learning-based personalized privacy protection scheme for spatial crowdsourcing. First, the proposed scheme constructs a personalized privacy protection model based on spatiotemporal features, which dynamically assigns personalized privacy protection intensity to different users by quantitatively analyzing the association between factors such as location, semantic sensitivity, access frequency, and access duration with individual privacy needs. Secondly, deep reinforcement learning algorithms are employed to adjust the model dynamically according to changes in the environment, thereby achieving an adaptive balance between privacy protection and task completion efficiency, and a bidirectional privacy-preserving mechanism combining Local Differential Privacy (LDP) and homomorphic encryption is designed to protect the privacy of both workers and requesters. Again, to achieve a more comprehensive task allocation strategy, this scheme proposes a multi-factor optimized task assignment mechanism that combines parameters such as worker reputation and task response latency. Finally, compared with the existing schemes, the proposed scheme can maintain a high accuracy rate for task assignment while ensuring personalized privacy protection, thereby improving the efficiency and reliability of the crowdsourcing system.

A trajectory privacy protection method using cached candidate result sets (TPP-CCRS) is proposed for the user trajectory privacy leakage problem. First, the user's area is divided into a grid to lock the user's trajectory range, and a cache area is set on the user's mobile side to cache the candidate result sets queried from the user's area. Second, a security center is deployed to register users securely and assign public and private keys for verifying location information . The same user's location information is randomly divided into M copies and sent to multi-anonymizers. Then, the random concurrent k -anonymization mechanism with multi-anonymizers is used to concurrently k -anonymize M copies of location information. Finally, the prefix tree is added on the location-based service (LBS) server side, and the location information is encrypted using the clustered data fusion privacy protection algorithm. The optimal binary tree algorithm queries user interest points. Security analysis and experimental verification show that the TPP-CCRS can effectively protect user trajectory privacy and improve location information query efficiency.

The proliferation of location-based service applications has led to a substantial surge in the amount of life trajectory data produced by mobile devices. And these data frequently contain confidential personal details. Simultaneously, the corresponding relatively lagging privacy protection technology and the improper trajectory data handling method will make tremendous problems with privacy breach. Therefore, this paper presents a trajectory privacy data publishing scheme, denoted as LORDP, which is based on local optimisation and R-tree. The proposed scheme aims to handle sensitive data while improves trajectory protection effectiveness. Firstly, the scheme combines the LKC-privacy model requirement to filter out the minimum violating sequences set, to reduce data sensitivity and the amount of injected noise. Secondly, R-tree is constructed based on trajectory similarity. Finally, Laplacian noise is added to the R-tree’s leaf nodes constrained by differential privacy. The experiments show that the proposed LORDP algorithm significantly enhances the utility of data compared to other algorithms, and reduces the loss rate of about approximately 2% for per trajectory data, which shows that the present algorithm is extremely effective.

A common misconception concerning Insider Threat is that the information infrastructure is at considerable risk from technical issues. In fact, Insider Threat is a multidisciplinary concept across many different fields, including personnel security, environment security and technology security. All aspects regarding Insider Threat must be addressed in a well-structured and holistic manner, failure of which may result in security breaches. The aim of this paper is to provide an integrated and holistic approach to establish a security and defense architecture for Insider Threat.