Anonymous Networks Research Articles

Tor network anonymity evaluation based on node anonymity

In order to address the shortcomings of traditional anonymity network anonymity evaluation methods, which only analyze from the perspective of the overall network and ignore the attributes of individual nodes, we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes, network behavior, and program security monitoring. The anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating status. The anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation features. This paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under attack. Our approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.

How user innovation communities contribute to sustainability transitions. An exploration of three online communities

Online user communities have given rise to a new form of peer network collaboration. This paper examines three user innovation communities to assess their contribution to sustainability transitions, particularly in terms of exploring sustainable technical solutions and their integration into society. We study three user innovation communities by analysing internet forum interactions and conducting member interviews. To assess the impact of these communities, we use the technological innovation systems framework as an analytical perspective. Our analysis shows that user communities encompass a wide range of innovation activities and show great variation in their profiles: from strongly technology-oriented, rather anonymous networks to very broad-based communities that actively influence policy discourse, public acceptance and the social embeddedness of technologies. We conclude that innovation policy makers should recognise the transformative potential of this type of innovation and harness its potential to open up and explore alternative and more sustainable pathways.

A Multi-Objective Degree-Based Network Anonymization Method

Enormous amounts of data collected from social networks or other online platforms are being published for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have motivated the work on degree-based data anonymization. In this paper, we propose and study a new multi-objective anonymization approach that generalizes the known degree anonymization problem and attempts at improving it as a more realistic model for data security/privacy. Our suggested model guarantees a convenient privacy level, based on modifying the degrees in a way that respects some given local restrictions, per node, such that the total modifications at the global level (in the whole graph/network) are bounded by some given value. The corresponding multi-objective graph realization approach is formulated and solved using Integer Linear Programming to obtain an optimum solution. Our thorough experimental studies provide empirical evidence of the effectiveness of the new approach, by specifically showing that the introduced anonymization algorithm has a negligible effect on the way nodes are clustered, thereby preserving valuable network information while significantly improving data privacy.

Post-Quantum Anonymous One-Sided Authenticated Key Exchange without Random Oracles

Authenticated Key Exchange (AKE) is a cryptographic protocol to share a common session key among multiple parties. Usually, PKI-based AKE schemes are designed to guarantee secrecy of the session key and mutual authentication. However, in practice, there are many cases where mutual authentication is undesirable such as in anonymous networks like Tor and Riffle, or difficult to achieve due to the certificate management at the user level such as the Internet. Goldberg et al. formulated a model of anonymous one-sided AKE which guarantees the anonymity of the client by allowing only the client to authenticate the server, and proposed a concrete scheme. However, existing anonymous one-sided AKE schemes are only known to be secure in the random oracle model. In this paper, we propose generic constructions of anonymous one-sided AKE in the random oracle model and in the standard model, respectively. Our constructions allow us to construct the first post-quantum anonymous one-sided AKE scheme from isogenies in the standard model.

Algorithms for Efficiently Computing Structural Anonymity in Complex Networks

This article proposes methods for efficiently computing the anonymity of entities in networks. We do so by partitioning nodes into equivalence classes where a node is k -anonymous if it is equivalent to k -1 other nodes. This assessment of anonymity is crucial when one wants to share data and must ensure the anonymity of entities represented is compliant with privacy laws. Additionally, in such an assessment, it is necessary to account for a realistic amount of information in the hands of a possible attacker that attempts to de-anonymize entities in the network. However, measures introduced in earlier work often assume a fixed amount of attacker knowledge. Therefore, in this work, we use a new parameterized measure for anonymity called d - k -anonymity. This measure can be used to model the scenario where an attacker has perfect knowledge of a node’s surroundings up to a given distance d . This poses nontrivial computational challenges, as naive approaches would employ large numbers of possibly computationally expensive graph isomorphism checks. This article proposes novel algorithms that severely reduce this computational burden. In particular, we present an iterative approach, assisted by techniques for preprocessing nodes that are trivially automorphic and heuristics that exploit graph invariants. We evaluate our algorithms on three well-known graph models and a wide range of empirical network datasets. Results show that our approaches significantly speed up the computation by multiple orders of magnitude, which allows one to compute d - k -anonymity for a range of meaningful values of d on large empirical networks with tens of thousands of nodes and over a million edges.

Supervised ranking approach to identify infLuential websites in the darknet

The anonymity and high security of the Tor network allow it to host a significant amount of criminal activities. Some Tor domains attract more traffic than others, as they offer better products or services to their customers. Detecting the most influential domains in Tor can help detect serious criminal activities. Therefore, in this paper, we present a novel supervised ranking framework for detecting the most influential domains. Our approach represents each domain with 40 features extracted from five sources: text, named entities, HTML markup, network topology, and visual content to train the learning-to-rank (LtR) scheme to sort the domains based on user-defined criteria. We experimented on a subset of 290 manually ranked drug-related websites from Tor and obtained the following results. First, among the explored LtR schemes, the listwise approach outperforms the benchmarked methods with an NDCG of 0.93 for the top-10 ranked domains. Second, we quantitatively proved that our framework surpasses the link-based ranking techniques. Third, we observed that using the user-visible text feature can obtain comparable performance to all the features with a decrease of 0.02 at NDCG@5. The proposed framework might support law enforcement agencies in detecting the most influential domains related to possible suspicious activities.

DIGITAL INFORMATION AS AN OBJECT OF EXPERT RESEARCH IN THE CONTEXT OF DIGITALIZATION: PROBLEMS AND DEVELOPMENT PROSPECTS

The article is devoted to the problems of considering digital information as an object of expert research. It is proposed to amend the current criminal procedural legislation by adding part 2 of Art. 84 of the Criminal Procedure Code of Ukraine with new sources of evidence – digital, namely, to state it in the following wording: «2. Procedural sources of evidence are testimony, material evidence, digital evidence, documents, and expert opinions. The definition of digital information is formulated in the context of the investigation of criminal proceedings, namely, it is indicated that it can be understood as data presented in electronic (digital) form, containing information relevant to the case, containing traces of a criminal offence that is an instrument or means of committing it, stored on tangible media: computer, mobile devices, digital cameras, routers, etc., or intangible resources such as the Internet, databases, local networks of institutions and organizations, etc., while on the specified media may contain: files (containing text information, audio or video); a variety of software used as a «tool» for committing a crime or to hide the traces of its commission; files containing traces of forgery or falsification; databases, free access to which is prohibited by the current legislation in the field of protection of state secrets, protection of personal data; scanned copies, photocopies of restricted documents, etc. Two groups of digital information are identified, which are the object of expert research in the course of the pre-trial investigation: digital information located on a material storage medium; digital information located on the Internet or in cloud storage. Attention is drawn to the forensic computer-technical examination of digital information located in the darknet, since persons carrying out criminal activities using it use a fairly extensive data protection system to exclude the possibility of identifying the offender by law enforcement agencies: «protected» operating systems are used: Linux, Kodachi Linux, etc.; browsers are used that are not widely used by consumers, such as, for example, Tor Browser, which allows you to establish an anonymous network connection that has some protection from outside interference; access to the Internet is carried out using a VPN in order to prevent the determination of the actual location of a person and his IP address.

Security and performance implications of BGP rerouting-resistant guard selection algorithms for Tor

Tor is the most popular anonymization network with millions of daily users. This makes it an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods preferring Tor nodes with higher resilience to active BGP attacks.In this work, we analyze the implications of such proposals and demonstrate that two state-of-the-art path selection methods, namely Counter-RAPTOR and DPSelect, are not as secure as thought before. First, we show that DPSelect achieves only one third of its originally claimed resilience and, thus, is not as resilient as widely accepted. Second, we reveal that the resilience to active BGP attacks on the way back, i.e., from the first anonymization node to a given Tor user, provided by both methods is significantly lower than on the forward path. Beside their lower resilience in specific cases, we show that for particular users the usage of Counter-RAPTOR and DPSelect also leads to leakage of user’s location. Furthermore, we uncover the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious Tor middle node can fingerprint the user’s location and the confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.

The LSTM-based automated phishing detection driven model for detecting multiple attacks on Tor hidden services

Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today’s challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments.

Four shades of deterministic leader election in anonymous networks

Four shades of deterministic leader election in anonymous networks

Percolation framework reveals limits of privacy in conspiracy, dark web, and blockchain networks

We consider the limits of privacy based on the knowledge of interactions in anonymous networks. In many anonymous networks, such as blockchain cryptocurrencies, dark web message boards, and other illicit networks, nodes are anonymous to outsiders, however the existence of a link between individuals is observable. For example, in blockchains, transactions between anonymous accounts are published openly. Here we consider what happens if one or more individuals in such a network are deanonymized by an outside investigator. These compromised individuals could then potentially leak information about others with whom they interacted, leading to a cascade of nodes’ identities being revealed. We map this scenario to percolation and analyze its consequences on three real anonymous networks—(1) a blockchain transaction network, (2) interactions on the dark web, and (3) a political conspiracy network. We quantify, for different likelihoods of individuals possessing information on their neighbors, p, the fraction of accounts that can be identified in each network. We then estimate the minimum and most probable number of steps to a desired anonymous node, a measure of the effort to deanonymize that node. In all three networks, we find that it is possible to deanonymize a significant fraction of the network (>50%) within less than 5 steps for values of p>0.4. We show how existing measures and approaches from percolation theory can help investigators quantify the chances of deanonymizing individuals, as well as how users can maintain privacy.

Few-shot website fingerprinting attack with cluster adaptation

Website fingerprinting attacks can destroy users’ privacy on the Internet, even when the communication is through an anonymous system, such as Tor. Deep learning-based website fingerprinting attacks have made great progress in anonymous traffic classification, and obtained high classification accuracy. However, deep website fingerprinting attacks require a large amount of training data with annotations. With the version update of anonymous network systems or network condition changing, previously collected traffic traces cannot be used for the classification of the newly collected traces, and recollecting traffic traces is time-consuming and labor-intensive. To address the above problems, this paper proposes Cluster Website Fingerprinting Attack (CWFA) for the few-shot website fingerprinting attack, which is based on the clustering assumption: samples belonging to the same cluster have the same category. CWFA utilizes deep neural networks to extract the trace features, and aligns the labeled category centers with unlabeled target cluster centers in the feature space. CWFA can preserve the category-level structure and facilitates the classification of the newly collected traffic traces. We conduct extensive experiments on public datasets in both closed-world and open-world scenarios, and we test the performance of the defended traces. Compared with the state-of-the-art WF methods, experiment results demonstrate the effectiveness and superiority of our proposed method.

HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks

Online social network datasets contain a large amount of various information about their users. Preserving users’ privacy while publishing or sharing datasets with third parties has become a challenging problem. The k-automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k-anonymity methods, including k-degree or k-neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k-automorphism one. The proposed algorithm is based on the structure of the previously published k-automorphism KM algorithm. However, it solves the NP-hard subtask of finding isomorphic graph extensions with a genetic algorithm and employs the GraMi algorithm for finding frequent subgraphs. In the design of the genetic algorithm, we introduce the novel chromosome representation in which the length of the chromosome is independent of the size of the input network, and each individual in each generation leads to the k-automorphism solution. Moreover, we present a heuristic method for selecting the set of vertex disjoint subgraphs. To test the algorithm, we run experiments on a set of real social networks and use the SecGraph tool to evaluate our results in terms of protection against deanonymization attacks and preserving data utility. It makes our experimental results comparable with any future research.

The Dark Web: Privacy and Anonymity

Abstract: The Internet as a whole is a network of multiple computer networks and their vast infrastructure. The network consists of websites accessible through search engines such as Google, Firefox, etc. It's called the Surface Web. The Internet is further subdivided into the Deep Web – content that is unindexed and inaccessible to traditional search engines. The Dark Web is considered part of the Deep Web. It is accessible via TOR. Participants on the dark web are anonymous and hidden. Anonymity, confidentiality and the possibility of not being detected are three factors offered by special browsers like TOR and I2P. In this article, we discuss the impact of the Darknet in different social areas and provide results. It gives the number of daily anonymous darknet users (with TOR) in Kosovo and worldwide at the time showed the impact of hidden service sites and got search engine results from Ahimia and Onion City Dark Web. Anonymity on the dark web is not sufficiently verified. TOR strives to provide anonymous activity and aims to provide anonymous activity. Here are the results for showing the number of users and their location. calculations are based on IP addresses by country code, and accesses the sources of those addresses and reports the numbers in aggregate form. In this way,dark web users are indirectly represented. Another key factor is the number of anonymous network users on the dark web. In such a network, users are counted based on requests from directory clients (via TOR metrics) and the relay list is updated. Indirectly, the number of users calculated for the anonymous networks.

Web Site Fingerprint Attack Generation Technology Combined with Genetic Algorithm

An anonymous network can be used to protect privacy and conceal the identities of both communication parties. A website fingerprinting attack identifies the target website for the data access by matching the pattern of the monitored data traffic, rendering the anonymous network ineffective. To defend against fingerprint attacks on anonymous networks, we propose a novel adversarial sample generation method based on genetic algorithms. We can generate effective adversarial samples with minimal cost by constructing an appropriate fitness function to select samples, allowing us to defend against several mainstream attack methods. The technique reduces the accuracy of a cutting-edge attack hardened with adversarial training from 90% to 20–30%. It also outperforms other defense methods of the same type in terms of information leakage rate.

Want to Gather? No Need to Chatter!

A team of mobile agents with different labels, starting from different nodes of an unknown anonymous network, must meet at the same node and declare that they all met. This task of gathering was traditionally considered assuming that agents at the same node can exchange information. We ask if this ability of talking is needed. The answer turns out to be no. We design two deterministic algorithms that accomplish gathering in a much weaker model. We only assume that each agent knows how many agents are at the node that it currently occupies. Our first algorithm assumes that agents know some upper bound N on the size of the network, and works in time polynomial in N and in the length of the smallest label. Our second algorithm does not assume any knowledge about the network, but its complexity is at least exponential in the size of the network and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product we solve, in the same weak model, the fundamental problem of leader election among agents. As an application we solve the gossiping problem in this model: if each agent has a message, all agents can learn all messages. This is perhaps our most surprising finding: agents without any transmitting devices can solve the most general information exchange problem if they can count the number of agents present at visited nodes.

Design of three-factor secure and efficient authentication and key-sharing protocol for IoT devices

The remarkable advance of the Internet of Things (IoT) has smoothed the way to the interconnection of various mobile devices in secure access and communication. In order to guarantee user privacy and anonymity in public networks, a large number of mutual authentication and key-sharing protocols between different IoT devices and multi-servers have been proposed. Due to resource-constrained and inefficient IoT devices, most previous protocols can confront assorted malicious attacks, such as eavesdropping, counterfeiting, chip cloning, device forgery, and other attacks. These attacks may be exposed the user’s private key or other sensitive data. To solve these problems, physical unclonable function (PUF) is a lightweight security primitive that utilizes random process deviations that cannot be controlled during chip manufacturing to generate device-unique digital signatures. In this paper, we combined the key-sharing scheme based on PUF on the hardware side, which solves relevant security problems such as device cloning and key tampering. Furthermore, we propose a three-factor secure and efficient authentication and key-sharing protocol, leveraging the inherent security properties of passwords, biometrics, and PUFs. We demonstrate the security of our proposed protocol based on computational Bilinear Diffie–Hellman Problem (BDH) and k-CAA hard problems and the Proverif tool. Compared with existing relevant protocols, our protocol meets various security properties and defends against varied security threats. The low computational cost, communication overhead, and device storage indicate that our protocol is applicable to resource-constrained IoT devices.

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet , an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.

On the Message Complexity of Fault-Tolerant Computation: Leader Election and Agreement

This paper investigates the message complexity of two fundamental problems, leader election and agreement in the crash-fault synchronous and fully-connected distributed network. We present randomized (Monte Carlo) algorithms for both the problems and also show non-trivial lower bounds on the message complexity. Our algorithms achieve sublinear message complexity in the so-called implicit version of the two problems when tolerating more than a constant fraction of the faulty nodes. In comparison to the state-of-art, our results improved and extended the works of [Gilbert-Kowalski, SODA'10] (which studied only the agreement problem) in several directions. Specifically, our algorithms tolerate any number of faulty nodes up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(n -\operatorname{polylog}n)$</tex-math></inline-formula> . The message complexity (and also the time complexity) of our algorithms is optimal (up to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\operatorname{polylog}n$</tex-math></inline-formula> factor). Further, our algorithm works in anonymous networks, where nodes do not know each other. To the best of our knowledge, these are the first sub-linear results for both the leader election and the agreement problem in the crash-fault distributed networks.

A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography

The spread of the Internet of Things (IoT) has caused a variety of devices to communicate with one another, producing enormous volumes of data and nurturing a linked ecosystem. However, the privacy of users, the integrity of the data, and secure authentication are seriously jeopardised by this link. In this context, we suggest a unique technique that makes use of identity-based anonymity and cryptographic authentication in IoT infrastructure in order to solve these problems.In order to facilitate safe communications between IoT devices, users, and service providers, our proposed mechanism creates a third-party authenticated protocol. This anonymity protection reduces the danger of unauthorised tracking and data exploitation by securing user names and usage patterns from possible attackers.The proposed system ensures the integrity and confidentiality of data transmitted between IoT devices and service providers by utilising sophisticated encryption algorithms. Additionally, it simplifies the authentication procedure, improving system efficiency overall without sacrificing security. The proposed technique into practise and assessed it using real-world IoT infrastructure scenarios, showcasing how well it protects user privacy and ensures safe communication. The findings show that the mechanism has a low computing cost and can withstand a range of security threats, making it a viable option for identity-based anonymity and authentication in IoT networks. Our approach makes a significant contribution to protecting user data and encouraging trustworthy interactions in IoT infrastructure as the IoT environment continues to grow.