The General Data Protection Regulation (GDPR) aims to enable the free flow of personal data while enhancing individual control. Integrating privacy-preserving data retrieval methods can provide stronger protection for personal privacy. However, existing approaches lack compliance mechanisms aligned with the GDPR, making it difficult in practice to simultaneously satisfy the principles of lawfulness and data minimization, while also exhibiting clear limitations in both security and efficiency. To address these problems, we propose VOLE-PDRAA, an efficient privacy-preserving data retrieval protocol with anonymous authorization based on the Vector-OLE (VOLE). Specifically, VOLE-PDRAA constructs a VOLE-blinded identifier by integrating pseudorandom linear encoding with VOLE-derived correlation vectors, enabling rigorous anonymity guarantees during authorization. Building on this, the protocol incorporates a non-interactive zero-knowledge proof (NIZK) to achieve anonymous authorization for the data subject and to generate verifiable informed consent proofs, thereby meeting the principle of lawfulness. Meanwhile, the data controller can verify whether each retrieval request falls within the scope authorized by the data subject without learning any identifiable information, thus maintaining adherence to the data-minimization principle in a post-quantum environment. Furthermore, VOLE-PDRAA utilizes labeled private set intersection (labeled-PSI) to safeguard the confidentiality of identifiers and their associated records under post-quantum security conditions, while enabling large-scale batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability (UC) framework. Experimental evaluation validates its superiority through comparison with state-of-the-art work.

In the data-driven era, the unchecked collection and processing of personal data has given rise to serious privacy concerns. In response, the General Data Protection Regulation (GDPR) was introduced to grant individuals stronger control over the use of their data. Privacy data retrieval methods show considerable promise in this context, but further improvements are required to balance the principles of lawfulness and data minimization. To address this problem, we propose PDRAA, an efficient privacy data retrieval protocol with anonymous authorization based on the verifiable credential (VC). Specifically, our designed VC achieves anonymous identification of data subjects and facilitates fine-grained access control by supporting selective disclosure of attributes. By combining VC with non-interactive zero-knowledge (NIZK) proofs, PDRAA enables data subjects to anonymously authenticate via VC presentation. This allows the data controller to verify the legitimacy of retrieval requests while ensuring compliance with the principle of data minimization. Besides, PDRAA introduces a re-randomization mechanism to prevent linkability attacks during the authorization process and provides lightweight, flexible authorization revocation. Moreover, we utilize Labeled Private Set Intersection (Labeled PSI) technology to meet the privacy requirements of participants and support batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability framework. Experimental results demonstrate that PDRAA outperforms existing methods in terms of performance, which is significant for promoting compliance with GDPR.

Abstract-Underwater Wireless Sensor Networks (UWSNs) can enable a broad range of applications; data aggregation is a fundamental task in such multi-hop wireless sensor networks. To the best of our knowledge, none of existing research works have addressed the interference-free data aggregation scheduling problem in UWSNs. In this paper, we formally define the data aggregation model in UWSNs. We propose a realistic aggregation scheduling scheme together with its theoretical latency bound Rh(C(Δ - 1) + D), where Rhand Δ are the hop radius and the max degree of the network respectively while C and D is a constant. Specifically, we introduce the concept of Virtual Slot to efficiently exploit multiplexing opportunities of time domain. Compared with naively adapted terrestrial algorithms, the evaluation results show that our proposed algorithm achieve far better performance especially when the packet size is small or the node density is high.

Dynamic application placement for clustered web applications heavily influences system performance and quality of user experience. Existing approaches claim that they strive to maximize the throughput, keep resource utilization balanced across servers, and minimize the start/stop cost of application instances. However, they fail to minimize the worst case of server utilization; the load balancing performance is not optimal. What's more, some applications need to communicate with each other, which we called dependent applications; the network cost of them also should be taken into consideration. In this paper, we investigate how to minimize the resource utilization of servers in the worst case, aiming at improving load balancing among clustered servers. Our contribution is two-fold. First we propose and define a new optimization objectives: limiting the worst case of each individual server's utilization, formulated by a min-max problem. A novel framework based on binary search is proposed to detect an optimal load balancing solution. Second, we define system cost as the weighted combination of both placement change and inter-application communication cost. By maximizing the number of instances of dependent applications that reside in the same set of servers, the basic load-shifting and placement-change procedures are enhanced to minimize whole system cost. Extensive experiments have been conducted and effectively demonstrate that: 1) the proposed framework achieves a good allocation for clustered web applications. In other words, requests are evenly allocated among servers, and throughput is still maximized; 2) the total system cost maintains at a low level; 3) our algorithm has the capacity of approximating an optimal solution within polynomial time and is promising for practical implementation in real deployments.