Dong Tong

Bio: Dong Tong is an academic researcher from Xidian University. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

A Topology Based Differential Privacy Scheme for Average Path Length Query

Abstract: Differential privacy is heavily used in privacy protection due to it provides strong protection against private data. The existing differential privacy scheme mainly implements the privacy protection of nodes or edges in the network by perturbing the data query results. Most of them cannot meet the privacy protection requirements of multiple types of information. In order to overcome these issues, a differential privacy security mechanism with average path length (APL) query is proposed in this paper, which realize the privacy protection of both network vertices and edge weights. Firstly, by describing APL, the reasons for choosing this attribute as the query function are analyzed. Secondly, global sensitivity of APL query under the need of node privacy protection and edge-weighted privacy protection is proved. Finally, the relationship between data availability and privacy control parameters in differential privacy is analyzed through experiments.

DOT-M: A Dual Offline Transaction Scheme of Central Bank Digital Currency for Trusted Mobile Devices (Extended Version)

Abstract: . In recent years, many major economies have paid close attention to central bank digital currency (CBDC). As an optional attribute of CBDC, dual offline transaction is considered to have great practical value under the circumstances for payment without network connection. However, there is no public report or paper on how to securely design or implement the dual offline transaction function specifically for CBDC. In this paper, we propose DOT-M, a practical dual offline transaction scheme designed for the mobile device user as either a payer or a payee. Precisely, adopting secure element (SE) and trusted execution environment (TEE), the architecture of trusted mobile device is constructed to protect security-sensitive keys and execution of the transaction protocol. According to the trusted architecture, the data structure for offline transaction is designed as well. On this basis, we describe the core procedures of DOT-M in detail, including registration, account synchronization, dual offline transaction, and online data updating. We also enumerate the exceptional situations that may occur during the dual offline transaction, and give specific handling methods for each situation. Moreover, six security properties of the scheme are analyzed under realistic assumptions. A prototype system is implemented and finally tested with possible parameters. The security analysis and experimental results indicate that our scheme could meet the practical requirement of CBDC offline transaction for mobile users from both aspects of security and efficiency.

DOT-M: A Dual Offline Transaction Scheme of Central Bank Digital Currency for Trusted Mobile Devices

Abstract: In recent years, many major economies have paid close attention to central bank digital currency (CBDC). As an optional attribute of CBDC, dual offline transaction is considered to have great practical value under the circumstances for payment without network connection. However, there is no public report or paper on how to securely design or implement the dual offline transaction function specifically for CBDC. In this paper, we propose DOT-M, a practical dual offline transaction scheme designed for the mobile device user as either a payer or a payee. Precisely, adopting secure element (SE) and trusted execution environment (TEE), the architecture of trusted mobile device is constructed to protect security-sensitive keys and execution of the transaction protocol. According to the trusted architecture, the data structure for offline transaction is designed as well. On this basis, we describe the core procedures of DOT-M in detail, including registration, account synchronization, dual offline transaction, and online data updating. A prototype system is implemented and finally tested with possible parameters. The security analysis and experimental results indicate that our scheme could meet the practical requirement of CBDC offline transaction for mobile users from both aspects of security and efficiency.

Statistical Properties of Sampled Networks

Abstract: We study the statistical properties of the sampled scale-free networks, deeply related to the proper identification of various real-world networks. We exploit three methods of sampling and investigate the topological properties such as degree and betweenness centrality distribution, average path length, assortativity, and clustering coefficient of sampled networks compared with those of original networks. It is found that the quantities related to those properties in sampled networks appear to be estimated quite differently for each sampling method. We explain why such a biased estimation of quantities would emerge from the sampling procedure and give appropriate criteria for each sampling method to prevent the quantities from being overestimated or underestimated.

ELPPS: An Enhanced Location Privacy Preserving Scheme in Mobile Crowd-Sensing Network Based on Edge Computing

Abstract: Mobile Crowd-Sensing (MCS) is gradually extended to the edge network to reduce the delay of data transmission and improve the ability of data processing. However, a challenge is that there are still loopholes in the protection of privacy data, especially in location-based services. The attacker can reconstruct the location relationship network among the correlation about the environment information, identity information, and other sensing data provided by mobile users. Moreover, in the edge environment, this kind of attack is more accurate and more threatening to the location privacy information. To solve this problem, we propose a location privacy protection scheme (ELPPS) for a mobile crowd-sensing network in the edge environment, to protect the position correlation weight between sensing users through differential privacy. We use the grid anonymous algorithm to confuse the location information in order to reduce the computing cost of edge nodes. The experiment results show that the proposed framework can effectively protect the location information of the sensing users without reducing the availability of the sensing task results, and has a low delay.

Cooperative Jamming Strategy Based on Community Detection for Two-Hop Communication Networks

Abstract: With a wide application of wireless hand-held devices in human life, the relationship among human beings may affect some wireless communication systems. Inspired by it, this paper investigates the influence of social relationships on the secrecy performance of a two-hop wireless communication system with multiple jammers. We model the secrecy problem of the whole transmission as a cooperative jamming game consisting of two independent sub-games, and design a cooperative jamming strategy based on the concept of community detection to seek the minimum secrecy outage probability among all Nash equilibriums. In our proposed strategy, each jammer is divided into different groups due to its relationships with the source, the relay and the destination. Simulations demonstrate that the proposed strategy can successfully have a good secrecy performance for the two-hop system and a high average utility.

Global and Local Differentially Private Release of Count-Weighted Graphs

Abstract: Many complex natural and technological systems are commonly modeled as count-weighted graphs, where nodes represent entities, edges model relationships between them, and edge weights define some counting statistics associated with each relationship. As graph data usually contain sensitive information about entities, preserving privacy when releasing this type of data becomes an important issue. In this context, differential privacy (DP) has become the de facto standard for data release under strong privacy guarantees. When dealing with DP for weighted graphs, most state-of-the-art works assume that the graph topology is known. However, in several real-world applications, the privacy of the graph topology also needs to be ensured. In this paper, we aim to bridge the gap between DP and count-weighted graph data release, considering both graph structure and edge weights as private information. We first adapt the weighted graph DP definition to take into account the privacy of the graph structure. We then develop two novel approaches to privately releasing count-weighted graphs under the notions of global and local DP. We also leverage the post-processing property of DP to improve the accuracy of the proposed techniques considering graph domain constraints. Experiments using real-world graph data demonstrate the superiority of our approaches in terms of utility over existing techniques, enabling subsequent computation of a variety of statistics on the released graph with high utility, in some cases comparable to the non-private results.