Path Privacy Research Articles

Multi‐hop anonymous payment channel network based on onion routing

AbstractPayment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.

SIoVChain: Time-Lock Contract Based Privacy-Preserving Data Sharing in SIoV

Social Internet of Vehicles (SIoV) is an emerging technology in the smart city environment, enabling smart vehicles to form social groups and exchange data among themselves. SIoV facilitates many applications aiming to improve driving safety and traffic monitoring by sharing data among vehicles. It ensures a safe and comfortable drive. However, privacy, data confidentiality, and data integrity are the major challenges during multi-hop data transfer that must be addressed for the wide adoption of SIoV. The existing solutions do not provide anonymity and consume more network resources. To address these issues, we propose SIoVChain, a time-lock contract-based privacy-preserving data sharing scheme with incentives for SIoV. It does not only enable data sharing between vehicles anonymously but incentivizes them also. In addition, the proposed framework imposes a penalty anonymously if a malicious vehicle disseminates false information. SIoVChain is shown to be secure against stealing processing fee attack while preserving sender-receiver privacy and path privacy. Universal Composability (UC) framework is used to verify user privacy. The feasibility and efficiency of the scheme are also demonstrated.

VALNET: Privacy-preserving multi-path validation

Network path validation (or simply, path validation) provides network end-hosts with the ability to enforce the network paths they want their packets to traverse. Path validation also enables each on-path node to validate whether a packet has followed the same path specified by the respective source node. Traditional networking uses single-path routing. Multi-path routing provides on-path nodes (including the source node) with the flexibility to choose a path, among a set of options available to them, in order to forward a packet to the same destination node. Multi-path validation combines these two notions: a source node, instead of choosing a single path, can choose a set of paths to send its packets to a destination node; and each of the other on-path nodes can verify whether a packet has indeed traversed one of the legitimate paths (the paths designated by the source) and can further select any of these legitimate paths downstream to forward packets to the destination node. The source node typically embeds the set of paths in a packet-header to enable path validation at each on-path node. However, this results in several privacy issues and makes the underlying network prone to certain attacks. In this work, we introduce privacy-preserving multi-path validation with two privacy notions: path privacy and index privacy. We design VALNET, a network that achieves privacy-preserving multi-path validation. We analyze the security as well as the performance of VALNET. VALNET finds numerous use cases (e.g., in secure and private military communications between base stations and on-field devices such as sensor nodes and drones), where data communications need to take place over multiple paths (enforced by the end-hosts) for enhanced reliability, and privacy of any of these paths must not be compromised even if some of the on-path nodes are compromised. To the best of our knowledge, VALNET is the first work that addresses privacy concerns in the context of multi-path validation.

The Latticed-Based Path Privacy Protection Aggregation Scheme for Internet of Vehicles

With the occurrence of information leakage events of online ride-hailing, the information security of Internet of Vehicles has been highly valued by the society. Once the information of the Internet of Vehicles is exposed by the online ride-hailing platform or stolen by hackers, it will pose a fatal threat to the personal and property safety of users, and even cause a major information security accident to the whole society. Based on a post-quantum cryptography system, this paper proposes a practical privacy protection scheme for ride-hailing route information, which can present the complete statistical aggregation operation of the route and the frequency from the starting point to the destination without the visibility of the ride-hailing platform, and ensure the data privacy security of a single vehicle. Compared with representative multi-vehicle aggregation solutions, we not only guarantee message privacy, confidentiality, integrity, forward and backward security, anti-man in attack and anti-redial attack, but also achieve multi-dimensional aggregation, CCA security and anti-quantum attack. Through the analysis of the experiment, the cost of our scheme is reasonable. Therefore, the scheme is more practical in this scenario.

Privacy-preserving Network Path Validation

The end-users communicating over a network path currently have no control over the path. For a better quality of service, the source node often opts for a superior (or premium) network path to send packets to the destination node. However, the current Internet architecture provides no assurance that the packets indeed follow the designated path. Network path validation schemes address this issue and enable each node present on a network path to validate whether each packet has followed the specific path so far. In this work, we introduce two notions of privacy— path privacy and index privacy —in the context of network path validation. We show that, in case a network path validation scheme does not satisfy these two properties, the scheme is vulnerable to certain practical attacks (that affect the privacy, reliability, neutrality and quality of service offered by the underlying network). To the best of our knowledge, ours is the first work that addresses privacy issues related to network path validation. We design PrivNPV, a privacy-preserving network path validation protocol, that satisfies both path privacy and index privacy. We discuss several attacks related to network path validation and how PrivNPV defends against these attacks. Finally, we discuss the practicality of PrivNPV based on relevant parameters.

Preserving adjustable path privacy for task acquisition in Mobile Crowdsensing Systems

Mobile Crowdsensing is an emerging and promising sensing paradigm in which sensor data can be collected by mobile users equipped with smart devices. In Mobile Crowdsensing Systems (MCS), workers bid for location-based sensing tasks and get rewards from the platform. However, the bidding may leak workers’ path privacy, which means the sensitive locations could be inferred from innocent locations along a path as workers continuously acquire for tasks. This privacy concern may significantly hinder the participation of workers. As a result, this paper designs a novel framework for adjustable path privacy preservation used for task acquisition in MCS. In this framework, workers are allowed to flexibly adjust their privacy preferences on the amount, sensitivity, and cost of private locations. Two algorithms are proposed to determine the set of bidding tasks for workers that jointly consider the privacy concerns and profits. The first algorithm processes in a centralized approach, which is proved to be rational, truthful and efficient. The second algorithm allows workers to decide their task acquisition locally, and guarantees the Nash equilibrium among workers. Both algorithms are validated via real-world dataset. The evaluation results demonstrate that the two proposed algorithms outperform baseline algorithms on both platform and worker sides.

SupAUTH: A new approach to supply chain authentication for the IoT

AbstractRecent advances of the Internet of Things (IoT) technologies have enhanced the use of radio‐frequency identification‐based tracking system to be widely deployed in supply chain management covering every step involved in the flow of merchandise from the supplier to the customer to ensure a trustworthy delivery environment. Such authentication system (also known as path authentication) not only guarantees the merchandise to be available in the right destination with no discrepancies and errors but also ensures the route of the merchandise progress to be valid. This paper outlines the current state‐of‐the‐art cryptographic solutions for path authentication, highlights their properties and weakness, and proposes a novel, privacy‐preserving, and efficient solution. Compared with the existing elliptic curve ElGamal re‐encryption–based solution, our homomorphic message authentication code on arithmetic circuit–based solution offers less memory storage (with limited scalability) and no computational requirement on the reader. Moreover, we allow computational ability inside the tag that articulates a new privacy direction to the state‐of‐the‐art path privacy. This privacy notion helps support the confidentiality of the tag movement in the context of IoT‐enabled cross‐organizational tracking environment where the stakeholders can be from different organizations associated together with the merchandise being delivered. As a potential extension to the path authentication protocol, we further propose a polynomial‐based mutual authentication as a security extension and batch initialization as an efficiency extension. Besides our brief security and privacy analysis, our evaluation shows that the proposed solution can significantly reduce memory requirements on tags with marginal computational overhead to ensure transmission path confidentiality. We observe that SupAUTH requires maximum 513‐bit tag memory and 57.3 ms of processing time during evaluation, which is not only practical but also suitable for any suitable low‐cost radio‐frequency identification deployment in IoT.

Erratum to: Dynamic path privacy protection framework for continuous query service over road networks

Erratum to: Dynamic path privacy protection framework for continuous query service over road networks

Dynamic path privacy protection framework for continuous query service over road networks

Many applications of location based services (LBSs), it is useful or even necessary to ensure that LBSs services determine their location. For continuous queries where users report their locations periodically, attackers can infer more about users' privacy by analyzing the correlations of their query samples. The causes of path privacy problems, which emerge because the communication by different users in road network using location based services so, attacker can track continuous query information. LBSs, albeit useful and convenient, pose a serious threat to users' path privacy as they are enticed to reveal their locations to LBS providers via their queries for location-based information. Traditional path privacy solutions designed in Euclidean space can be hardly applied to road network environment because of their ignorance of network topological properties. In this paper, we proposed a novel dynamic path privacy protection scheme for continuous query service in road networks. Our scheme also conceals DPP (Dynamic Path Privacy) users' identities from adversaries; this is provided in initiator untraceability property of the scheme. We choose the different attack as our defending target because it is a particularly challenging attack that can be successfully launched without compromising any user or having access to any cryptographic keys. The security analysis shows that the model can effectively protect the user identity anonymous, location information and service content in LBSs. All simulation results confirm that our Dynamic Path Privacy scheme is not only more accurate than the related schemes, but also provide better locatable ratio where the highest it can be around 95 % of unknown nodes those can estimate their position. Furthermore, the scheme has good computation cost as well as communication and storage costs.Simulation results show that Dynamic Path Privacy has better performances compared to some related region based algorithms such as IAPIT scheme, half symmetric lens based localization algorithm (HSL) and sequential approximate maximum a posteriori (AMAP) estimator scheme.

$$k^{-}$$ k - -anonymization of multiple shortest paths

The preservation of privacy on information networks has been studied extensively in recent years. Although early work concentrated on preserving sensitive node information and link information to prevent re-identification attacks, recent development has instigated a focus on preserving sensitive edge weight information such as shortest paths. Two types of privacy on edge weights have been proposed. One type of privacy attempts to add random noise edge weights to the graph while still maintaining the same shortest path. The other type of privacy, k-shortest path privacy, minimally perturbs edge weights so that there are at least k shortest paths. However, there might be insufficient paths that can be modified to the same path length. In this work, we present a new concept, called \( k^{-}\)-shortest path privacy, to allow anonymizing different numbers of shortest paths for different sources and destination vertex pairs. For a given privacy level k and a pair of source and destination vertices with \(k_i\) paths between the two vertices, we propose a greedy-based algorithm trying to modify Never-Visited, Partially-Visited, and All-Visited edges, in sequence, from top\(-k\) shortest paths (or \(k_i\) available paths) so that all possess the same path length after modification. We use the weighted-proportional-based strategy to modify the edge weights. We also define a new privacy value to quantify the privacy level of \(k^{-}\)-shortest path privacy. Numerical experiments showing the characteristics of the proposed algorithm are given. Comparison to k-shortest path privacy demonstrates that the proposed approach is more efficient and flexible than the previous model.

Edge types vs privacy in K-anonymization of shortest paths

Information breaches in social networks and other published data have caused many concerns of privacy issues in recent years. Since information in networks can be modeled as graphs, various techniques have been proposed to preserve privacy of sensitive data on directed/un-directed, weighted/un-weighted graphs. To preserve privacy on graphs, most works for publishing anonymized social networks have attempted to prevent unique patterns to be re-identified, such as, nodes, links, and sub-graphs. In this work, we study the problem of anonymizing sensitive shortest paths in graphs. We examine the concept of k-shortest path privacy, in which at least k indistinguishable shortest paths exist between specified sensitive source and destination vertices. Due to the overlaps of shortest paths, we propose three algorithms to modify three categories of edges to achieve the k-shortest path privacy. Numerical experiments showing the characteristics of the proposed algorithms are given. The results demonstrate that the proposed algorithms are all feasible to achieve k-shortest path privacy, with different degrees of execution times and information losses, and can be served as design reference for shortest path anonymization.

K-anonymous path privacy on social graphs

Growing popularity of social networking not only brings the convenience of information sharing but also concerns of privacy breaches. Information on social networks can be modeled as un-weighted or...

K-anonymous path privacy on social graphs

Growing popularity of social networking not only brings the convenience of information sharing but also concerns of privacy breaches. Information on social networks can be modeled as un-weighted or weighted graph data. To preserve privacy, k-anonymity on relational, set-valued, and graph data have been studied extensively in recent years. In this work, we consider the edge weight anonymity problem. In particular, to protect the weight privacy of the shortest path between two vertices on a weighted graph, we present a new concept called k-anonymous path privacy. A published social network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices. Three greedy-based modification algorithms, based on modifying different types of edges, to achieve k-anonymous path privacy are proposed. Experimental results showing the feasibility and characteristics of the proposed approach are presented. The proposed techniques clearly provide different options to achieve the same level of privacy under different requirements.

SHORTEST PATHS ANONYMIZATION ON WEIGHTED GRAPHS

Due to the proliferation of online social networking, a large number of personal data are publicly available. As such, personal attacks, reputational, financial, or family losses might occur once this personal and sensitive information falls into the hands of malicious hackers. Research on Privacy-Preserving Network Publishing has attracted much attention in recent years. But most work focus on node de-identification and link protection. In academic social networks, business transaction networks, and transportation networks, etc, node identities and link structures are public knowledge but weights and shortest paths are sensitive. In this work, we study the problem of k-anonymous path privacy. A published network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices [21]. In order to achieve such privacy, three different strategies of modification on edge weights of directed graphs are proposed. Numerical comparisons show that weight-proportional-based strategy is more efficient than PageRank-based and degree-based strategies. In addition, it is also more efficient and causes less information loss than running on un-directed graphs.