Anonymous Scheme Research Articles

TCα-PIA: A Personalized Social Network Anonymity Scheme via Tree Clustering and α-Partial Isomorphism

Social networks have become integral to daily life, allowing users to connect and share information. The efficient analysis of social networks benefits fields such as epidemiology, information dissemination, marketing, and sentiment analysis. However, the direct publishing of social networks is vulnerable to privacy attacks such as typical 1-neighborhood attacks. This attack can infer the sensitive information of private users using users’ relationships and identities. To defend against these attacks, the k-anonymity scheme is a widely used method for protecting user privacy by ensuring that each user is indistinguishable from at least k−1 other users. However, this approach requires extensive modifications that compromise the utility of the anonymized graph. In addition, it applies uniform privacy protection, ignoring users’ different privacy preferences. To address the above challenges, this paper proposes an anonymity scheme called TCα-PIA (Tree Clustering and α-Partial Isomorphism Anonymization). Specifically, TCα-PIA first constructs a similarity tree to capture subgraph feature information at different levels using a novel clustering method. Then, it extracts the different privacy requirements of each user based on the node cluster. Using the privacy requirements, it employs an α-partial isomorphism-based graph structure anonymization method to achieve personalized privacy requirements for each user. Extensive experiments on four public datasets show that TCα-PIA outperforms other alternatives in balancing graph privacy and utility.

Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means

While anonymization techniques have improved greatly in allowing data to be used again, it is still really hard to get useful information from anonymized data without risking people’s privacy. Conventional approaches such as k-Anonymity and Differential Privacy have limitations in preserving data utility and privacy simultaneously, particularly in high-dimensional spaces with manifold structures. We address this challenge by focusing on anonymizing data existing within high-dimensional spaces possessing manifold structures. To tackle these issues, we propose and implement a hybrid anonymization scheme termed as the (β, k, b)-anonymization method that combines elements of both differential privacy and k-anonymity. This approach aims to produce high-quality anonymized data that closely resembles real data in terms of knowledge extraction while safeguarding privacy. The Fréchet mean, an operation applicable in metric spaces and meaningful in the manifold setting, serves as a key aspect of our approach. It provides insight into the geometry of data points within high-dimensional spaces. Our goal is to anonymize this Fréchet mean using our proposed approach and minimize the distance between the original and anonymized Fréchet mean to achieve data privacy without significant loss of information. Additionally, we introduce a novel Fréchet mean clustering model designed to enhance the clustering process for high-dimensional spaces. Through theoretical analysis and practical experiments, we demonstrate that our approach outperforms traditional privacy models both in terms of preserving data utility and privacy. This research contributes to advancing privacy-preserving techniques for complex and non-linear data structures, ensuring a balance between data utility and privacy protection.

Enhancing privacy and security in 5G networks with an anonymous handover protocol based on Blockchain and Zero Knowledge Proof

The latest advancement in cellular technology, 5G, has the ability to facilitate various applications such as Device-to-Device (D2D) communication, vehicular communications, and the Internet of Everything (IoE). However, the technology is faced with challenges related to user privacy, resource management, handover authentication, and security of the air interface and internet channels. Moreover, the use of specialized base stations such as wireless Access Points (AP) and Road-Side Units (RSU) in vehicular communications may be owned by subscribers outside formal network operators. The conventional handover authentication schemes have a lengthy latency period that conflicts with the 5G quality of service. To tackle these challenges, this paper presents an anonymous scheme that utilizes Blockchain and Zero Knowledge Proof (ZKP) to ensure efficient privacy in the handover protocol of 5G Networks. The proposed scheme has been thoroughly evaluated for security and privacy and has shown to be resistant to identity catching, location area tracing, and replay attacks while achieving Forward/Backward secrecy. Furthermore, the performance evaluation of the proposed scheme indicates that it is efficient and speedy as it involves a limited number of messages, low computation, and does not necessitate the involvement of the Home Network (HN) in the authentication or handover protocols. A diligently crafted BAHO authentication protocol, following cryptographic principles, guarantees robust security against the Real-or-Random model. This results in a seamless and secure transition for User Equipment, upholding confidentiality, integrity, and authenticity during handovers between Access Points.

A Comprehensive Approach to User Delegation and Anonymity within Decentralized Identifiers for IoT.

Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users' digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.

Blacklisting Based Anonymous Authentication Scheme for Sharing Economy

Authentication and blacklisting mechanisms have a key role for service providers to deliver the service to correct users through digital channels. Nevertheless, there always have been concerns about privacy of the users against such mechanisms. The conditional anonymity concept is proposed as a remedy to these concerns. A recent approach in the literature for conditional anonymity is blacklistable anonymous credentials, which allows service providers to blacklist users for an authentication session without identifying the user. In this paper, we improve user anonymity in conditionally anonymous schemes using two complementary mechanisms. First, we define whitelisting property for blacklistable anonymous credentials and give a construction of this scheme. The whitelisting property can be used to unlink an honestly behaved authentication session from the user. Second, we propose an extension of this scheme for a particular use case, sharing economy services. This scheme allows a service provider to blacklist a user only if the user have not returned the shared asset in due time. We benchmark the performance of our schemes by comparing them with the rival schemes. Our experiments show that both of our scheme have comparable performance to previous works.

ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce Systems

ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce Systems

MLDA: a multi-level k-degree anonymity scheme on directed social network graphs

MLDA: a multi-level k-degree anonymity scheme on directed social network graphs

A Sensitivity-based Location Privacy Protection Scheme in Vehicular Networks

In recent times, the issue of vehicle location privacy has received increasing attention. Location-based services (LBSs) require users’ location information to be constantly updated to service providers, which causes the location information to be speculated and attacked by malicious entities. The pseudonym schemes offer a viable solution to the aforementioned problem, but existing pseudonym schemes do not provide differentiated protection for users’ varying locations, thereby increasing the possibility of location information leakage. To address this concern, we propose a sensitivity-based pseudonym exchange mechanism, which leverages the vehicle’s historical track record to extract features and enable customized location privacy protection. Performance evaluation results demonstrate that our approach significantly outperforms existing approaches in achieving location privacy.

A novel lightweight decentralized attribute-based signature scheme for social co-governance

Social co-governance is an effective way to solve public safety incidents such as COVID-19 incident. But the insiders wouldn’t share the firsthand information for fear of cyber-violence. Though most of researches paid attention to the data access control and security, less attention focused on the identity privacy. So, in this work, it constructed a lightweight decentralized attribute-based signature scheme with unconditional full anonymity (LDABS-UFA) scheme to protect user’s identity privacy security. It confuses the user’s private key with fuzzy factors, and prevents user’s privacy in the signature from being disclosed. It outsources some complex computing to cloud servers to reduce the user device performance overhead. The security analysis result shows that the scheme is UFA security, unforgeability and non-collusion. Besides, a social co-governance system based on lightweight blackbox-based traceable decentralized attribute-based signature (LWBT-DABS) scheme is proposed. By generating fingerprints for the data owner in the signature, regulators can monitor the entire system and prevent malicious user from spreading rumors. And it constructs an accountability mechanism to prevent the abuse of supervisory power. The performance analysis shows that the data signature of our scheme size is constant, and the scheme is feasibility and practicability.

Formula omitted]ODNS: A distributed approach to DNS anonymization with collusion resistance

The traditional Domain Name System (DNS) lacks fundamental security and privacy features in its design. As privacy concerns increased on the Internet, security and privacy enhancements of DNS have been actively investigated. Specifically, in the context of user privacy in DNS queries, several relay-based anonymization schemes have been recently introduced. However, these schemes are vulnerable to collusion between relays and full-service resolvers, which means user identities cannot be hidden from resolvers. This paper introduces a new concept for achieving user anonymity in DNS queries through a multiple-relay-based approach, called μODNS (Mutualized Oblivious DNS), by extending the concept of existing relay-based schemes. μODNS introduces a reasonable assumption that each user has at least one trusted or dedicated relay within the network and mutually shares the relay with other users. The user simply sets his trusted relay as the next-hop relay to convey his queries to the resolver and randomly chooses its zero or more subsequent relays shared by other entities. Under this assumption, the user’s identity remains concealed from the target resolver in μODNS even if an unknown subset of relays colludes with the resolver. Namely, in μODNS, users can preserve their anonymity by paying a small cost of sharing their resources. Additionally, we extend existing protocols, Anonymized DNSCrypt and Oblivious DoH, to provide practical Proof-of-Concept specifications and implementations as instances of μODNS. These implementations are publicly available on the Internet as open-source software and public services. Furthermore, we demonstrate, through measurements of round-trip times for DNS messages, that our implementation can minimize the performance degradation resulting from its privacy enhancements, achieving performance levels that maintain the positive user experiences observed in existing schemes.

Retracted: A Novel Variable Pseudonym Scheme for Preserving Privacy User Location in 5G Networks

Retracted: A Novel Variable Pseudonym Scheme for Preserving Privacy User Location in 5G Networks

Speaker anonymization using generative adversarial networks

The advent use of smart devices has enabled the emergence of many applications that facilitate user interaction through speech. However, speech reveals private and sensitive information about the user’s identity, posing several security risks. For example, a speaker’s speech can be acquired and used in speech synthesis systems to generate fake speech recordings that can be used to attack that speaker’s verification system. One solution is to anonymize the speaker’s identity from speech before using it. Existing anonymization schemes rely on using a pool of real speakers’ identities for anonymization, which may result in associating a speaker’s speech with an existing speaker. Hence, this paper investigates the use of Generative Adversarial Networks (GAN) to generate a pool of fake identities that are used for anonymization. Several GAN types were considered for this purpose, and the Conditional Tabular GAN (CTGAN) showed the best performance among all GAN types according to different metrics that measure the naturalness of the anonymized speech and its linguistic content.

A multiple sensitive attributes data publishing method with guaranteed information utility

AbstractData publishing methods can provide available information for analysis while preserving privacy. The multiple sensitive attributes data publishing, which preserves the relationship between sensitive attributes, may keep many records from being grouped and bring in a high record suppression ratio. Another category of multiple sensitive attributes data publishing, which reduces the possibility of record suppression by breaking the relationship between sensitive attributes, cannot provide the sensitive attributes association for analysis. Hence, the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility. To acquire a guaranteed information utility, this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes. A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss. The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes. The proposed method can guarantee information utility when compared with previous ones.

DARVAN: A fully decentralized anonymous and reliable routing for VANets

With the recent advances in the automobile industry and the emergence of smart self-driving vehicles, the increasing need for proper and optimal Vehicular Ad-hoc Network (VANet) protocols has drawn the attention of many researchers. Nevertheless, the VANet community has yet to agree on a unified and suitable set of protocols for such networks. One of the major concerns in these networks is security considerations, which are significant because of the specific characteristics of VANet. In this article, we aim to address the location privacy and reliability issues in VANet routing protocols. We propose DARVAN, a fully decentralized infrastructure that provides anonymous and reliable routing in VANets. By employing a distributed database and collective consensus, DARVAN minimizes the exposure of critical data, which is conventionally stored and processed in centralized units. DARVAN is deployed by modifying the I2P protocol, which aims to improve routing reliability and resilience to many adversary activities in VANets. Specifically, with DARVAN, we present an effective and efficient network-level mitigation for Sybil attacks in Vanets. Our extensive simulations on NS3 show that DARVAN performs well in terms of packet delivery ratio, overhead, delay, and reliability compared to the previous anonymous scheme proposed for VANet routing.

A Traceable Self-Generated Pseudonym Scheme in VANETs

The generation of the pseudonym in vehicular ad hoc networks (VANETs) usually requires the participation of a trusted center (TA), and it needs multiple interactions with the TA. To address these problems, we propose a novel traceable self-generated pseudonym based on the non-interactive zero-knowledge proof (NIZK) and consortium blockchain in VANETs. In our scheme, a self-generated pseudonyms method is introduced to eliminate excessive communication burden, and a consortium blockchain is composed of multiple roadside units (RSUs) to replace the TA. RSUs in the consortium blockchain verify the effectiveness of OBU’s self-generated pseudonyms through the consensus mechanism. Multiple trace authorities (TRAs) are designed to jointly tracking of the real identity of the OBU, which effectively prevents the abuse of supervision by the only supervisor.

A Privacy Conserves Pseudonym Acquisition Scheme in Vehicular Communication Systems

Vehicular communication systems rely on temporary anonymous identities, i.e., pseudonyms, to avoid the possibility of tracking vehicles. If a vehicle uses only one pseudonym, an adversary can easily follow the vehicle by observing and linking messages signed under that pseudonym. Therefore, the vehicles acquire a set of pseudonyms from the Pseudonym Certificate Authority. If a vehicle would be unable to obtain these pseudonyms because of the bad communication and delay in processing from the roadside unit, it would not utilize the communication system without compromising the privacy of location. Hence, security and privacy techniques proposed for conventional vehicular ad-hoc network’s pseudonym-changing strategy may not scale well in such early deployed scenarios. This paper addresses the above-mentioned problem by offering a secure pseudonym scheme. Under this scheme, a vehicle can generate its pseudonyms using Gao Algorithm. Pseudonym Certificate Authority (PCA) issues one pseudonym for one vehicle that is sent to vehicles after encryption. The Gao Algorithm automatically generates sub-pseudonyms that are useable for 10 days. Consequently, the PCA requires less memory while providing maximum location confidentiality, and there is almost no complexity in managing pseudonyms. The Gao Algorithm uses encrypted identities so that only the central server knows the real identities of vehicles. We conduct simulations to verify the performance of the Gao Algorithm. Results show that the Gao Algorithm is expressively better than current strategies. Besides, even in the absence of roadside unit connections and low traffic on the road, the Gao Algorithm can achieve promising performance.

A Traceable and Anonymous Data Aggregation Scheme with Fog Computing Devices for Smart Grid

As an extension of the Internet of Things (IoT), smart city is a new aim for applications used in different industrial aspects. Intelligent IoT devices are used everywhere in smart cities to implement the functions such as monitoring and managing. Smart grid is one of the critical parts. Obtaining the quantity and cost of electricity usage is the most critical task of the smart grid. But such data in plaintext may leak the user privacy. So, it is an emergent aim to protect the private information of the user. Thus, we present a secure scheme for the smart grid, which not only protects the user’s information including identity and power consumption in the communications but also tracks the correct electricity cost for each user. Also, electricity consumption data from users could be aggregated in fog devices and then analyzed by the utility provider. The formal proof shows that the message transmission reaches the security level of the chosen plaintext attack (CPA). Also, the security properties of the scheme express the robustness. Finally, the performance study demonstrates that the proposed protocol is acceptable in practice.

Traffic Equilibrium Problems with Cross-Boundary Traffic: A Tradable Credit Approach

This study studies the tradable credit scheme design problem considering a mixture of local traffic and cross-boundary traffic. The local traffic refers to the travel demand generated by local residents with O-D pairs inside the network, while the cross-boundary traffic is the traffic with either origin or destination or both be outside the network. As the local authority aims to maximize its local social welfare, it determines the quantity of cross-boundary trips by evaluating the revenue of the cross-boundary traffic. Two credit charging schemes are investigated, i.e., a spatially differentiated credit scheme and an anonymous tradable credit scheme. In the first scheme, due to the different charging prices and the selfishness of the local authority, the travel credits are freely tradable within the local travellers only. The cross-boundary travellers have to buy travel credits from the local authority. In the second scheme, the tradable credit scheme is anonymous. The local authority determines the link-specific number of credits to be charged for using that link, while the travel credits are distributed to local travellers only but are allowed for free trading among both local and cross-boundary travellers. Two standard multi-class traffic equilibrium problems are established with side constraints and a credit restriction constraint. The equilibrium link flow patterns under these credit schemes are then demonstrated with local elastic demand. In both tradable credit schemes, the credit price in the trading market is unique under the equilibrium condition.

Data anonymization evaluation for big data and IoT environment

The growth of big data can increase risks of re-identification in complex IoT environment. Data anonymization is widely used to prevent shared data from being re-identified private or sensitive information from anonymized data with other available data. It is very important to understand the risks of re-identification of results and process of commonly used anonymization techniques before properly mitigating. This work proposes a data anonymisation evaluation framework that can evaluate commonly used data anonymization algorithms in terms of privacy preserving level, data utility, and performance. Specifically, data utility, privacy metrics, and information loss were used to evaluate data anonymization schemes that can further optimise the data anonymization process to keep risks of re-identification low. Experiment results demonstrate that the proposed solution can effectively evaluate the performance and de-identification features that can help to prevent inappropriate usage of anonymized data.

Multisource Data Hiding in Digital Images

In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding.