User Anonymity Research Articles

A Secure and Efficient Authentication Protocol for Satellite-Terrestrial Networks

The satellite-terrestrial networks (STNs) network has the characteristics of open links, node movement, dynamic network topology, and diverse collaborative algorithms, which will lead to frequent passive handovers and continuous re-authentication of user equipment. This causes a waste of valuable computing and storage resources in the STNs network, and seriously affects the user’s network experience. To address this, we propose an access authentication protocol with user anonymity and traceability to reduce the communication delay and signaling cost of access authentication. In addition, we also propose a hierarchical group key distribution scheme to implement cross-domain handover authentication between different groups of user equipment, thereby effectively avoiding re-authentication. We take strict security analysis to prove that the authentication protocol we propose is secure. Compared with the three related existing arts, our protocol can greatly reducing the communication interaction delay and improve the efficiency of initial access and handover authentication of user equipment.

XAuth: Secure and Privacy-Preserving Cross-Domain Handover Authentication for 5G HetNets

5G networks are highly heterogeneous, with ultradense base stations (BSs), due to the low penetration of millimeter waves and the availability of different access technologies. However, the continuous heterogeneity and densification of 5G networks pose great challenges to network security, especially for user mobility support. In the process of user handover between BSs or between different network domains, user access authentication and security session establishment are far riskier compared to 4G networks. On one hand, the overhead of handover authentication increases significantly as handovers become more frequent in an ultradense network. On the other hand, the differentiation of security schemes in heterogeneous networks poses a big challenge to handover authentication. Successfully designing a secure, privacy-preserving, and efficient handover authentication protocol for heterogeneous and ultra-dense 5G networks would substantially expand the prospects of future 5G network applications. Although numerous solutions (e.g., challengeresponse-based, public key cryptography-based, physical layer information-based, and blockchain-based solutions) have been proposed to solve the cross-domain handover authentication problem, most of them surfer from security and privacy vulnerabilities and unreasonable performance overhead. In this paper, we propose XAuth, a secure and privacy-preserving authentication protocol for both intra-domain and inter-domain handover in 5G Heterogeneous Networks (HetNets) based on blockchain. The proposed protocol can achieve mutual authentication, key agreement between User Equipment (UE) and target network, and is characterized by forward secrecy, backward secrecy, user anonymity, and conditional privacy preservation. Formal security analysis and comprehensive performance evaluation demonstrate the security and effectiveness of the proposed protocol.

Urgensi Perlindungan Hukum Perusahaan Exchanger Cryptocurrency Terhadap Aliran Transaksi dari Pengguna Anonim Wallet Decentralize Exchange

In its development, the use of crypto assets as an option for investing has shifted to be used as a new mode of money laundering. Cryptocurrency Exchange Companies have a strategic role in terms of preventing the occurrence of criminal acts of money laundering through crypto assets, this is because cryptocurrency exchange companies as an entry point and as an initial screening for the occurrence of buying and/or buying crypto assets carried out by actors. Cryptocurrency Exchange Companies are required to be reporting parties to Suspicious Financial Transactions (TKM) in TPPU. Data analysis in this study uses qualitative descriptive analysis methods with legal, statutory, and conceptual protection approaches. The results of this study state that the Need for Legal Protection for Cryptocurrency Exchange Companies as the receiving party to the flow of transactions from anonymous users (consisting of only one line of wallet addresses with a unique combination of letters and numbers) Wallet Decentralize Exchange (Dex) is necessary for a good faith Cryptocurrency Exchange Company to report every existing transaction, even though the reporting format cannot include an identity Service users, get guaranteed legal protection so as not to be subject to sanctions, both administrative sanctions and criminal sanctions. The form of legal protection that can be given to cryptocurrency exchange companies as reporting parties, is divided into 3 (three), namely Protection due to the guarantee of the Law in the form of Release from civil and criminal claims, Protection Due to the Implementation of the TPPU Law in the form of keeping the identity of the reporting party confidential, Special Protection by the National Police of the Republic of Indonesia in the form of personal security from physical and mental threats, to property, disguise of identity and Giving information without face to face (confrontation) with the suspect or defendant.

Session-based recommendation with hypergraph convolutional networks and sequential information embeddings

Session-based recommendation focuses on predicting the next item that an anonymous user is most likely to click. Due to its privacy-protecting ability, it is receiving increasing attention from researchers in recent years. The existing studies typically focus on sequence or graph structure learning. However, they ignore the consistency and sequential dependence relationships that are widely existed between items in real-world scenarios. To address this problem, we present a novel method named HyperS2Rec. Specifically, we propose to learn two kinds of item embeddings with hypergraph convolutional network and gated recurrent unit, respectively, to account for both consistency-awareness and sequential dependence-awareness. Then the attention mechanism is designed to flexibly combine the above both embeddings. Finally, the reversed position and the soft attention mechanism are utilized to obtain session representations. To verify the effectiveness of the proposed HyperS2Rec, we conduct experiments on three real-world datasets. The results prove that the proposed HyperS2Rec significantly outperforms state-of-the-art methods. The source code of our proposed model is available at https://github.com/ZZY-GraphMiningLab/HyperS2Rec.

A Secure and Anonymous Authentication Protocol Based on Three-Factor Wireless Medical Sensor Networks

Wireless medical sensor networks (WMSNs), a type of wireless sensor network (WSN), have enabled medical professionals to identify patients’ health information in real time to identify and diagnose their conditions. However, since wireless communication is performed through an open channel, an attacker can steal or manipulate the transmitted and received information. Because these attacks are directly related to the patients’ lives, it is necessary to prevent these attacks upfront by providing the security of WMSN communication. Although authentication protocols are continuously developed to establish the security of WMSN communication, they are still vulnerable to attacks. Recently, Yuanbing et al. proposed a secure authentication scheme for WMSN. They emphasized that their protocol is able to resist various attacks and can ensure mutual authentication. Unfortunately, this paper demonstrates that Yuanbing et al.’s protocol is vulnerable to smart card stolen attacks, ID/password guessing attacks, and sensor node capture attacks. In order to overcome the weaknesses and effectiveness of existing studies and to ensure secure communication and user anonymity of WMSN, we propose a secure and anonymous authentication protocol. The proposed protocol can prevent sensor capture, guessing, and man-in-the-middle attacks. To demonstrate the security of the proposed protocol, we perform various formal and informal analyses using AVISPA tools, ROR models, and BAN logic. Additionally, we compare the security aspects with related protocols to prove that the proposed protocol has excellent security. We also prove the effectiveness of our proposed protocol compared with related protocols in computation and communication costs. Our protocol has low or comparable computation and communication costs compared to related protocols. Thus, our protocol can provide services in the WMSN environment.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts

Federated authentication, such as Google ID, enables users to conveniently access multiple websites using a single login credential. Despite this convenience, securing federated authentication services requires addressing a single point of failure, which can result from using a centralized authentication server. In addition, because the same login credentials are used, anonymity and protection against user impersonation attacks must be ensured. Recently, researchers introduced distributed authentication schemes based on blockchains and smart contracts (SCs) for systems that require high availability and reliability. Data on a blockchain are immutable, and deployed SCs cannot be changed or tampered with. Nonetheless, updates may be necessary to fix programming bugs or modify business logic. Recently, methods for updating SCs to address these issues have been investigated. Therefore, this study proposes a distributed and federated authentication scheme that uses SCs to overcome a single point of failure. Additionally, an updatable SC is designed to fix programming bugs, add to the function of an SC, or modify business logic. ProVerif, which is a widely known cryptographic protocol verification tool, confirms that the proposed scheme can provide protection against various security threats, such as single point of failure, user impersonation attacks, and user anonymity, which is vital in federated authentication services. In addition, the proposed scheme exhibits a performance improvement of 71% compared with other related schemes.

CAP2M..÷Contingent Anonymity Preserving Privacy Method for the Internet of Things Services

The Internet of Things (IoT) consists of a collection of inter-connected devices that are used to transmit data. Secure transactions that guarantee user anonymity and privacy are necessary for the data transmission process. One of the major constraints of IoT Healthcare is the issue of maintaining security. These problems are addressed in this study by the Contingent Anonymity-Preserving Privacy Method (CAP2M), which takes into account the various security needs. The CAP2M method uses the session deployment and user requirements to preserve user privacy. The initial and final privacy settings are modified in the session deployment depending on the session interval and the service provider's recommendation. Federated learning is employed for training privacy settings toward the session's final interval. The suggested approach maintains privacy on various sharing intervals with minimum computation complexity. In the privacy-preserving process, two-factor authentication is used for concealing the sharing and shared ends. The performance is validated using the metrics failure, complexity, and latency.

Time Controlled Expressive Predicate Query With Accountable Anonymity

Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This “all-or-nothing” authorization mode is not compatible with the “pay-as-you-use” commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.

Exploring the Spatial Variation of Access/Egress Distances of Subway Stations Using Mobile Phone Positioning Data in Chengdu, China

The distance from the origin or destination to or from the subway station is defined as the access or egress distance, which determines the service coverage of the subway station. However, little literature studies the distances at the station level, and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variation of the distances at the station level by using the mobile phone positioning data of more than 1.2 million anonymous users in Chengdu, China. First, this study proposes a method to extract the access and egress trips of the subway. Next, the ordinary least squares (OLS) regression models are carried out to select the significant explanatory variables. Finally, the geographically weighted regression (GWR) models are used to model the spatial variation relationship between the 85th percentile access/egress distances and the selected explanatory variables. The results show that different stations’ access/egress distances vary significantly in space. Hotel, residence, life, finance, road density, and mixed land use are found to be negatively correlated with distances, while education, 36–45 years old, male, and high education are positively correlated. In addition, the GWR model reveals that the influence of explanatory variables on access/egress distance varies from space to space. The results further promote the understanding of the existing system and provide a relevant reference for planners and transportation departments to optimize land use and public transportation planning.

Characterization of the recently detected cathinone N-cyclohexyl butylone: From structure elucidation to in silico supported pharmacological/toxicological considerations

One of the most widely consumed new psychoactive substances (NPS) families in Europe are synthetic cathinones. Cathinone structure can be easily modified resulting in new derivatives that rapidly reach drug markets. In this work, the recently detected synthetic cathinone N-cyclohexyl butylone has been characterized by gas chromatography coupled to mass spectrometry (GC–MS), liquid chromatography coupled to high-resolution mass spectrometry (HRMS), nuclear magnetic resonance (NMR) and Fourier-transformed infrared (FTIR) spectroscopy, using research chemicals samples collected by the drug analysis service Energy Control from anonymous users. Compound identification was performed by the combination of HRMS and NMR data. The elemental composition and putative moieties of the compound were determined based on the accurate-mass ions observed by HRMS. Then, different NMR experiments, including bidimensional, allowed the establishment of the chemical structure and confirmation of compound identity. Furthermore, FTIR spectrum was also acquired in order to provide a complete analytical characterization of the novel cathinone. Finally, pharmacological/toxicological characterization was attempted using in silico methods. Based on these predictions, N-cyclohexyl butylone probably has similar effects to stimulants like MDMA.

Road User Position and Speed Estimation via Deep Learning from Calibrated Fisheye Videos.

In this paper, we present a deep learning processing flow aimed at Advanced Driving Assistance Systems (ADASs) for urban road users. We use a fine analysis of the optical setup of a fisheye camera and present a detailed procedure to obtain Global Navigation Satellite System (GNSS) coordinates along with the speed of the moving objects. The camera to world transform incorporates the lens distortion function. YOLOv4, re-trained with ortho-photographic fisheye images, provides road user detection. All the information extracted from the image by our system represents a small payload and can easily be broadcast to the road users. The results show that our system is able to properly classify and localize the detected objects in real time, even in low-light-illumination conditions. For an effective observation area of 20 m × 50 m, the error of the localization is in the order of one meter. Although an estimation of the velocities of the detected objects is carried out by offline processing with the FlowNet2 algorithm, the accuracy is quite good, with an error below one meter per second for urban speed range (0 to 15 m/s). Moreover, the almost ortho-photographic configuration of the imaging system ensures that the anonymity of all street users is guaranteed.

AQRS: Anti-quantum ring signature scheme for secure epidemic control with blockchain

Epidemics, such as Corona Virus Disease 2019 (COVID-19), have serious consequences globally, of which the most effective way to control the infection is contact tracing. Nowadays, research related to privacy-preserving epidemic infection control has been conducted, nevertheless, current researchers do not regard the authenticity of records and infection facts as well as poor traceability. Moreover, with the emergence of quantum computing, there is a bottleneck in upholding privacy, security and efficiency. Our paper proposes a privacy-preserving epidemic infection control scheme through lattice-based linkable ring signature in blockchain, called AQRS. Firstly, our scheme adopts a blockchain with three ledgers to store information in a distributed manner, which offers transparency and immunity from the Single Point of Failure (SPoF) and Denial of Service (DoS) attacks. Moreover, we design a lattice-based linkable ring signature scheme to secure privacy-preserving of epidemic infection control. Significantly, we are the first to introduce the lattice-based linkable ring signature into privacy preserving in epidemic control scenario. Security analysis indicates that our scheme ensures unconditional users anonymity, record unforgeability, signature linkability, link non-slanderability and contact traceability. Finally, the comprehensive performance evaluation demonstrates that our scheme has an efficient time-consuming, storage consumption and system communication overhead and is practical for epidemic and future pandemic privacy-preserving.

Cross-view temporal graph contrastive learning for session-based recommendation

Session-based recommendation (SBR) aims at recommending items given the behavior sequences of anonymous users in a short-term session. Many recent SBR methods construct all sessions as a global graph that captures cross-session item transition patterns (i.e., users’ global preference) to alleviate the problem of session data sparsity. However, these methods neglect that users’ interests will drift as the time between the sessions constantly increases, limiting the performance improvement of SBR. To fill this gap, we divide all sessions into a group of time-slices and model the cross-session item transitions for every time-slice. We further construct two augmentation views (i.e., temporal graph and temporal hypergraph views) to model pairwise and high-order item transitions on SBR. In addition, we construct contrastive learning between two views to improve the recommendation performance by maximizing the mutual information between the item representations learned from the two views. Experiments on three public real-world datasets (i.e., Diginetica, Retailrocket, and Yoochoose) show that our model is consistently superior to the other state-of-the-art baselines, especially in time-sensitive datasets. For instance, our model achieves 14.42% and 11.72% improvements in terms of P@10 and P@20 on the Diginetica dataset, respectively.

Multi-perspective enhanced representation for effective session-based recommendation

Session-based recommendation uses the click history of anonymous users to recommend the next possible click item for a given point in time. GNN-based models have returned promising results. However, these models still have some limitations. First, when GNN encodes the session data into session graphs, the order of the items is ignored; second, the GNN layer limit makes it impossible to capture some long-term dependencies in the session; third, noise items in the final generated session embedding will interfere with the model’s predictions. We propose a new multiperspective enhanced session representation recommendation model (MPER) to address the above problems. First, we introduce learnable position embedding to record the sequential relationships of the items, and propose the SLD-encoder to capture richer global information. Second, we propose an adaptive sparse target attention mechanism to alleviate the effect of the noise items in the traditional target embeddings. Third, a linear integration layer is added to the model to ensure that the session and item embeddings are in the same representation space. We conduct experiments on three real datasets, and the experimental results show that our model outperforms the SOTA baseline studies.

Traceable one-time address solution to the interactive blockchain for digital museum assets

Blockchain systems often use public keys or addresses as pseudonym accounts to protect the identity of users. However, as the blockchain system is transparent, an adversary can analyze all the public keys or addresses and obtain some real information about users, making the pseudonym mechanism ineffective. CryptoNote V2.0 is the first blockchain system to introduce a one-time address technology to enable the true anonymity of a user. But it does not have rigorous security proof. Moreover, due to application requirements, for one thing, users need anonymity. For another, administrators need to trace the identity of anonymous users. Therefore, a traceable one-time address scheme for the interactive system of digital museum items is proposed to fulfill these concerns. It allows the recipient to receive transactions anonymously, and a supervisor can trace the identity of a user efficiently. Two security models are defined and two corresponding instances are constructed. We prove that our instances are secure in IND-PK-CPA/IND-PK-CCA mode, respectively. Analysis and experiments show that the proposed schemes are effective in tracing the long-term identity of a recipient. Finally, the challenges of user privacy protection are enhanced with the advancement of the integration of digital museum asset interactions with blockchain. We build a blockchain system for Digital Museum assets and implement our two scheme instances in it. The system can integrate museum heritage resources around the world, share data based on blockchain, provide a large number of interactions and collections, and achieve large-scale exhibitions.

Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

The current industrial scenario has witnessed the application of several artificial intelligence-based technologies for mining and processing IoMT-based big data. An emerging distributed machine learning paradigm, Federated Learning (FL), has been widely applied in IoMT-based systems as a measure to overcome the issues associated with incorporating AI into such lightweight distributed computing systems while tackling privacy issues as well. However, extensive research has identified that classical FL is still prone to privacy threats due to data leakage and the chances of adversarial attacks during gradient transfer operations. Inspired by these issues, we propose a privacy-preserving framework (Fed_select) that ensures user anonymity in IoMT-based environments for big data analysis under the FL scheme. Fed_Select utilizes alternative minimization to limit gradients and participants in system training to decrease points of system vulnerability. The framework works on an edge computing-based architecture which ensures user anonymity via the employment of hybrid encryption techniques along with added benefits of load reduction at the central server. Also, a Laplacian noise-based differential privacy is employed on the shared attributes for security enhancement that adds confidentiality to the transferred data even during adversarial scenarios. Experimental results on standard datasets showcase that the change in the volume of gradients shared and the number of participants is not proportional to the variation in various system performance parameters. Specifically, an idealistic range of client and gradient-sharing fractions along with the appropriate value of noise for differential privacy implementation is determined. Additionally, we analyze the system from a security perspective as well as compare it with other schemes.

Use of Virtual Currencies for Financing Terrorism

Given the dynamic development of new technologies, it is becoming necessary to improve the system of counteracting and combating the financing of terrorism. In particular, this concerns virtual currencies. The use of them by the criminal world is encouraged by decentralisation of that market, anonymity of users and broad opportunities for functioning on the darknet, which is frequently beyond control of any services. In February 2020, the value of the cryptocurrency market was estimated at US$300 billion. In Poland, the first legal regulations on counteracting the financing of terrorism with the use of virtual currencies were adopted as late as in 2018, and under the 2021 amendment the legislator introduced several other regulations relating to that area.

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Unveiling the Veiled: Leveraging Deep Learning and Network Analysis for De-Anonymization in Social Networks

Online anonymity allows individuals to safeguard their personal information. It provides the freedom for anyone to express themselves without being concerned about censorship, discrimination, or retaliation. This pseudonym also provides opportunities for individuals to promote open discourse and diverse viewpoints. However, in today's digital age, this online anonymity has become a growing concern. Although the safeguarding of people’s rights, the advancement of free speech, and the development of a more diverse and democratic online community all depend heavily on the anonymity of online users, there are risks as well, such as cyberbullying, harassment, and the propagation of false information. Because of their anonymity, predators, groomers, and other unscrupulous individuals may be able to take advantage of vulnerable individuals, especially children and adolescents. In order to trick victims into hazardous or abusive situations, adversaries can hide their identities. Deep learning and network analysis can be used to reveal the true identities of anonymous social media users in order to combat this. Deep learning algorithms are capable of analyzing a wide range of social network data, including user behavior, relationships, and content, in order to find patterns and correlations that can lead to the true identities of users that go anonymous. The proposed system includes examining network topology and group dynamics to identify potential anomalies and connections that could lead to de-anonymization. This paper proposes a novel module for de-anonymization integrating deep learning and network analysis in social networks.

On Modern Crime – Money Laundering and Cryptocurrencies

The article concerns the practice of money laundering with the use of virtual currencies. This phenomenon is gaining in importance because it is a relatively easy way to legalise unlawfully obtained assets. The relative anonymity of users and the constant technological development favour criminal activity and the development of new strategies. The article introduces the concepts of money laundering and cryptocurrencies, and analyzes the procedure indicated.