Real-world Identity Research Articles

Blockchain Forensics: A Systematic Literature Review of Techniques, Applications, Challenges, and Future Directions

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.

Advanced Privacy Scheme to Improve Road Safety in Smart Transportation Systems

In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.

A Proposed High-Level Methodology on How OSINT is applied in Blockchain Investigations

The characteristics of blockchain established a desirable platform for entities to innovate and operate in a secure, transparent, and decentralised manner. However, cybercriminals have increasingly found refuge in the decentralised environment of blockchain technology. Cryptocurrencies are increasingly misused in malicious activities that encompass the trade of illicit goods, money laundering, various types of scams and ransomware attacks. The total cryptocurrency value received by illicit addresses reached an all-time high of $20.6 billion in 2022 according to Chainalysis. The inherent privacy and anonymity features of many blockchain networks make it challenging for law enforcement and regulatory agencies to track and apprehend wrongdoers. Consequently, a pressing need arises not only to initiate investigations on the blockchain to identify unlawful activities, but also to discover connections between these activities and the identities of the responsible individuals. Due to blockchain data being publicly available, the application of Open-Source Intelligence (OSINT) techniques is proposed to facilitate these types of investigations. In the context of blockchain, OSINT, together with investigation tools hold the promise of unearthing valuable information that could aid in attributing malicious activities to the individuals responsible for those actions. By analysing and synthesizing data from publicly accessible sources, such as data from blockchain explorers and link analysis tools such Chainalysis, Maltego or Spiderfoot, investigators could potentially unveil valuable clues that assist in building a comprehensive picture of blockchain-related criminal activities. Ultimately, with sufficient information and actionable intelligence collected, the main goal is to link it to Know Your Customer (KYC) data, that could be obtained from cryptocurrency exchanges via a subpoena from law enforcement agencies. This paper delves into the mechanisms of various OSINT tools and techniques, to determine their adaptability to the specific demands of blockchain investigations. This study provides a methodology and recommendations with insights into how these tools can be wielded to bridge the gap between blockchain's pseudonymity and real-world identities.

Unsupervised clustering of bitcoin transactions

Since its inception in 2009, Bitcoin has become and is currently the most successful and widely used cryptocurrency. It introduced blockchain technology, which allows transactions that transfer funds between users to take place online, in an immutable manner. No real-world identities are needed or stored in the blockchain. At the same time, all transactions are publicly available and auditable, making Bitcoin a pseudo-anonymous ledger of transactions. The volume of transactions that are broadcast on a daily basis is considerably large. We propose a set of features that can be extracted from transaction data. Using this, we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm, according to the transaction properties. Finally, according to these properties, we are able to characterize these clusters and the transactions they include. Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses. Using the novel features we introduce, our work classifies transactions in multiple clusters, while previous studies only attempt binary classification. Results indicate that most transactions fall into a cluster that can be described as common user transactions. Other clusters include transactions made by online exchanges and lending services, those relating to mining activities as well as smaller clusters, one of which contains possibly illicit or fraudulent transactions. We evaluated our results against an online database of addresses that belong to known actors, such as online exchanges, and found that our results generally agree with them, which enhances the validity of our methods.

Anonymity in decentralized apps: Study of implications for cybercrime investigations

In the digital age, cybercrime facilitated by anonymous communication apps raises significant concerns. Criminals exploit the anonymity provided by these apps, creating challenges for law enforcement and cybersecurity professionals when investigating and combating cybercrime. The complexity of decentralized applications (DApps) without centralized servers further complicates evidence certification. Although anonymity features to protect privacy, they impede the establishment of connections between digital accounts and real-world identities. In centralized server environments, data access for investigations is relatively straightforward. However, this study reveals that DApps present challenges due to decentralized control, anonymity, encrypted communication, and jurisdictional issues. DApps designed for anonymous communication allow users to interact without revealing their identities, making it challenging to trace criminals. While cybercrime investigations in centralized environments involve systematic evidence collection, correlation, analyzing communication patterns, collaboration with agencies, tracking IP addresses, legal authorization, and forensic analysis of digital devices, DApps-based investigations require vital intelligence gathering through open-source techniques (OSINT). This includes retrieving digital footprints, analyzing social media profiles, and tracing ownership information. Moreover, investigators may exploit human vulnerabilities, engage in deceptive communication, or use social engineering techniques to gather information while carefully considering the balance between user privacy and investigative requirements. In this study, we explore the many facets of anonymity in DApps and what challenges they impose for the investigation of cybercrime. The anonymity of users and their transactions in the context of new blockchain and decentralized technology presents difficulties for law enforcement. In the end, our research helps shed light on the complex relationship between anonymity in decentralized systems and the need for fairness online.

CEAT: Categorising Ethereum Addresses’ Transaction Behaviour with Ensemble Machine Learning Algorithms

Cryptocurrencies are rapidly growing and are increasingly accepted by major commercial vendors. However, along with their rising popularity, they have also become the go-to currency for illicit activities driven by the anonymity they provide. Cryptocurrencies such as the one on the Ethereum blockchain provide a way for entities to hide their real-world identities behind pseudonyms, also known as addresses. Hence, the purpose of this work is to uncover the level of anonymity in Ethereum by investigating multiclass classification models for Externally Owned Accounts (EOAs) of Ethereum. The researchers aim to achieve this by examining patterns of transaction activity associated with these addresses. Using a labelled Ethereum address dataset from Kaggle and the Ethereum crypto dataset by Google BigQuery, an address profiles dataset was compiled based on the transaction history of the addresses. The compiled dataset, consisting of 4371 samples, was used to tune and evaluate the Random Forest, Gradient Boosting and XGBoost classifier for predicting the category of the addresses. The best-performing model found for the problem was the XGBoost classifier, achieving an accuracy of 75.3% with a macro-averaged F1-Score of 0.689. Following closely was the Random Forest classifier, with an accuracy of 73.7% and a macro-averaged F1-Score of 0.641. Gradient Boosting came in last with 73% accuracy and a macro-averaged F1-Score of 0.659. Owing to the data limitations in this study, the overall scores of the best model were weaker in comparison to similar research, with the exception of precision, which scored slightly higher. Nevertheless, the results proved that it is possible to predict the category of an Ethereum wallet address such as Phish/Hack, Scamming, Exchange and ICO wallets based on its transaction behaviour.

Transformative live-action roleplay and Dagorhir costumes: Regulation, consumption and power dynamics, 1977 to the present

Dagorhir is one of the largest and oldest documented live-action roleplay groups. Dagorhir organizers have published multiple game regulations via handbooks with much emphasis on costumes since the 1970s. Dagorhir facilitates community building, identity negotiation and creative storytelling that expands beyond the game through transformative play. In our research, we examine how these costume regulations have influenced fantasy character and real-world identities, how the regulations have influenced perceived costume authenticity over time and how the handbook regulations have engaged with power dynamics related to intersectional identities. We analysed costume-related content in the three handbooks while drawing upon content analysis and historical methods. We found that as the regulations evolved since the 1970s, the rules increasingly centred costumes, indicating the prominence of costume in this escapist community. However, while these spaces centred on the costumed body, Dagorhir regulations reinforced oppressive intersectional norms. Our work has implications for society and business, that is, our findings can help individuals understand why people participate in live-action roleplay, which may reduce stigma surrounding this activity. Additionally, costume producers and retailers can make informed business decisions based upon our findings. Last, live-action roleplay communities can utilize our findings to reject oppressive written and unwritten regulations.

Becoming others

By revisiting Gilles Deleuze's notion of the virtual, this work examines what the virtual world means for our identity and intimacy. We sketch three effects of the virtual world: 1) the complete disintegration of public-private boundary; 2) the emerging performativity of virtual identities; 3) the destabilization of our ontology. From public life to multiplayer online role-playing games, we show the changing meaning of play in various contexts: performing, acting, and becoming. Following Richard Sennett's dramaturgical perspective of the public Man, we see people proactively perform the image to construct the real-world identity. Then in the video games, players choose a virtual identity to act in the game world, transforming it into a liminal space where continued identity transitions become possible. We analyze several cases, including Otherkin and Otaku subculture, to argue that virtual identity and community can bring ontological indeterminacy and propose a program that coexists with otherness.

MultiJAF: Multi-modal joint entity alignment framework for multi-modal knowledge graph

Entity Alignment (EA) is a crucial task in knowledge fusion, which aims to link entities with the same real-world identity from different Knowledge Graphs (KGs). Existing methods have achieved satisfactory performance, however, they mainly focus on single modal KG, which is difficult to be effectively applied to multi-modal scenes. In this paper, we propose a Multi-modal Joint entity Alignment Framework (MultiJAF), which can effectively utilize the knowledge of various modalities. Concretely, we first learn the embeddings of different modalities, i.e., structure, attribute and image modalities. Next, we adopt an attention-based multi-modal fusion network to integrate these embeddings and use obtained joint embeddings to compute a joint embedding-based similarity matrix SJ. Moreover, we design a Numerical Process Module (NPM) to infer a similarity matrix SN according to the numerical information of entities. In the end, we utilize a simple late fusion method to ensemble two similarity matrices for the final alignment. In addition, to reduce the cost of labeling data, we propose a novel NPM-based unsupervised multi-modal EA method. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed MultiJAF.

Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph

Entity alignment, as the vital technique for knowledge graph construction and integration, aims to match entities that refer to the same real-world identity in different knowledge graphs (KGs). Recently, much effort has been devoted to embedding-based methods for entity alignment. For most of such methods, the entity with a high degree is hard to be aligned with its equivalent counterpart with a low degree. This degree difference between equivalent entities poses a great challenge for entity alignment. To solve this problem, a novel entity alignment framework that integrates a graph convolutional network (GCN) based embedding initializer and a degree aware generative adversarial network is proposed. In particular, the embedding initializer utilizes a GCN with highway gates to generate the preliminary embedding of entities based on their topological characteristics in the KGs. By alleviating the relevance between embeddings and degree features, the degree aware GAN mitigates the impact of degree difference and generates the final degree level irrelevant alignment result. To quantify the heterogeneity between the KGs, an evaluation metric called heterogeneity entropy of degree (HED) that represents the degree difference is defined in this paper. Based on HED, the impact of KG heterogeneity on the performance of the proposed DAGCN model is investigated based on WK3l-15k and DBP15k datasets. The experimental results show that the proposed degree aware adversarial graph convolutional network (DAGCN) outperforms other state-of-the-art methods over all metrics, especially when the HED is large.

Face processing in police service: the relationship between laboratory-based assessment of face processing abilities and performance in a real-world identity matching task

In the present study, we investigated whether police officers’ performance in searching for unfamiliar faces in a video-based real-world task is predicted by laboratory-based face processing tests that are typically used to assess individual differences in face processing abilities. Specifically, perceptual performance in the field was operationalized via the identification of target individuals in self-made close-circuit television (CCTV) video tapes. Police officers’ abilities in the laboratory were measured by the Cambridge Face Memory Test long form (CFMT+). We hypothesized that the CFMT+ predicts individual differences in the CCTV task performance. A total of N = 186 police officers of the Rhineland-Palatinate State Police participated in the study (i.e., N = 139 novice and advanced cadets with either 3 months, 15 months or 24 months of pre-service experience; N = 47 experienced police officers with three years of pre-service experience and at least two years of full-service experience, who participated in the assessment center of the special police forces, specifically the surveillance and technical unit). Results revealed that the CFMT+ explained variance in the CCTV task. In sample 1, CFMT+ scores predicted hits, but not false alarms. In contrast, in sample 2, CFMT+ scores were correlated with both hits and false alarms. From a theoretical perspective, we discuss factors that might explain CCTV task performance. From a practical perspective, we recommend that personnel selection processes investigating individual differences of police officers’ face processing abilities should comprise of two steps. At first, laboratory-based tests of face processing abilities should be applied. Subsequently, to validate laboratory-based individual differences in face processing abilities, we recommend that work samples such as CCTV tasks from the field should be added.

Breakthrough percepts of online identity: Detecting recognition of email addresses on the fringe of awareness.

A key issue facing cybercrime investigations is connecting online identities to real-world identities. This paper shows that by combining the Fringe-P3 method with a concealed information test, we can detect a participant's familiarity with their own email address, thus connecting their real-world identity to their online one. Participants were shown Rapid Serial Visual Presentation (RSVP) streams of email addresses, some including their own email address (probe) or a target email address. Familiarity with the probe was accurately detected with significant results at the group level and for 7 of 11 participants at the individual level. These promising results demonstrate that the method can be successfully used to detect online identities. Factors that may affect how well an email address probe stands out in the RSVP streams are also discussed.

A Systematic Research Review of Internet Addiction and Identity

A small but growing body of research documents how internet misuse can lead to negative outcomes for individuals. One particularly important area of public concern is the connection between addictive internet usage and human identity. This study addressed the connection between excessive internet use on real-world and virtual-world identity. This systematic research review synthesized research studies conducted between 2008 to 2018 on the influence of internet addiction on identity. Findings revealed that the majority of published studies focused on young individuals aged 9-30 years old (89%) and that the connection between excessive internet use on real-world identity and virtual-world identity was complex and multi-faceted. Online gaming addiction was identified as a leading theme within the published research (30%). Based on study findings, recommendations are made for greater future research attention to internet addiction among adults, comprehensive studies of the relationship between online and offline identity to internet addiction, and the inclusion of internet addiction as a multifaceted disorder in future editions of DSM, which includes online gaming addiction among other forms of online addiction.

What’s holding back blockchain finance? On the possibility of decentralized autonomous finance

Despite the past decade’s rapid innovation in adapting blockchain technology to new uses, financial intermediation remains elusive except in basic and highly collateralized forms. We introduce the concept of the technical frontier to delimit the kinds of interactions that can feasibly be structured algorithmically among pseudonymous agents, as on a blockchain, and show that lending and financial intermediation – unlike monetary exchange – lie outside it, even in simple forms. The path forward for truly blockchain-native financial applications, therefore, must involve the integration of real-world identity information in order to disincentivize defection. We discuss several potential technologies for doing so, and conclude that such integration is possible without compromising pseudonymity, provided real-world identity is available in the breach.

Blokzincir Teknolojisinin Dijital Kimlik Yönetiminde Kullanımı: Bir Sistematik Haritalama Çalışması

Çevrimiçi sunulan birçok ürün ve hizmete internet üzerinden erişerek işlem gerçekleştirmek için kullanılması zorunlu olan dijital kimlik, gerçek dünya kimlikleri ile güvenli biçimde ilişkilendirildiğinde insanların gerçekten iddia ettikleri kişi olduklarını kanıtlamalarını sağlamakta olup dijitalleşmenin en önemli unsurlarından biridir. Dijital kimliklerin tasarımı, kullanılması, yönetimi ve güvenliği gibi konularda mevcut uygulamalarda bazı sorunlar yaşanmaktadır. Bu sorunlardan en önemlileri arasında kimlik sistemlerinin devletler ya da büyük teknoloji şirketleri gibi merkezi otoritelerin kontrolünde olması, bu durumun verilerin usulsüz kullanımı ve mahremiyetin ihlal edilmesi risklerini doğurması, aynı bilgilerin farklı otoritelerce tutulması zorunluluğundan kaynaklanan veri tutarsızlıkları ve verimsiz iş süreçleri sayılabilir. Blokzincir teknolojisinin yapısal özellikleri mevcut sistemdeki bu verimsizlik ve problemleri gidermek için oldukça uygundur. 2008 yılında ilk ortaya çıkmasından bu yana sürekli olarak artan bir ilgiyle takip edilen ve uygulama sahası sürekli olarak genişleyen blokzincir teknolojisi konusunda hem devletler hem de Birleşmiş Milletler ve Avrupa Birliği gibi uluslararası kuruluşlar ve özel sektör tarafından dijital kimlik özelinde araştırma, model ve prototip geliştirme çalışmaları yürütülmektedir. Bu çalışmada, blokzincir teknolojisinin dijital kimlik alanında kullanımına ilişkin çalışmalar ve elde edilen çıktılar hakkında bütünsel bir fikir edinmek ve geleceğe yönelik bir perspektif sunmak amacıyla, sistematik haritalama metodu kullanılarak konu incelenmiştir.

Open Secrecy: How Police Crackdowns and Creative Problem-Solving Brought Illegal Markets out of the Shadows

Abstract Can organized illegal activities grow stronger and more advanced in response to legal pressure? In October 2013, the FBI shut down Silk Road, a thriving e-commerce market for illegal drugs. After the shock, market actors adopted a new identity verification method that enabled mass-migration to other markets, and created websites for information distribution that reduced post-shock uncertainties. The outcome was a decentralized market in which actors could operate in “open secrecy” across multiple websites. With verifiable pseudonyms and securely obfuscated real-world identities, actors could publicly discuss, plan, and participate in illegal activities. Threats from police and opportunistic criminals persisted but were no longer crippling concerns as buyers and sellers could reasonably expect that their exchange partners would be available for future business; the illegal market could operate more like a legal one. Drawing on quantitative and qualitative data, the author argues that advances in information technology have expanded the opportunity structure for cooperation and creative problem-solving in the underworld, and therefore that shocks did not hinder but rather stimulate development in digital drug markets. Data, collected in 2013–2017, include nearly one million transactions from three illicit e-commerce markets, three million messages from eight discussion forums, and website traffic from two market-independent websites.

Cryptocurrency and Privacy - An Introduction to the Interface

The largest and best-known Cryptocurrency in the global economy is Bitcoin but it is only one of approximately 2,000 cyptocurrencies in circulation today. A Bitcoin was worth 8,790.51 U.S. dollars as of March 4, 2020 and all the Bitcoins in the world were worth roughly $160.4 billion. In Bitcoin you don’t need to explicitly register or reveal your real world identity, but the pattern of your behavior might itself be identifying. This is the fundamental privacy question in a Cryptocurrency like Bitcoin leading to emergence of Privacy coins such a Monero and Zcash that utilize complex cryptography to achieve the greater Privacy levels with more anonymity features. However, in the zest for increased Privacy with focus on anonymity in Cryptocurrency transaction, the law and order issues are compromised wherein it would be near to impossible for the law enforcement agencies to track criminals dealing in money laundering, terrorist financing, tax evasion and other frauds by using Crypto currencies. In this background, it is necessary to understand “Cryptocurrency and Privacy” as an interface to better comprehends the subject of Cyptocurrency which is very dynamic in technology with multiple global implications on the economic and legal front. The present work aims to study Cryptocurrency in the context of Privacy. The foundational concepts and definitions of the two competing subjects: ‘Cryptocurrency and Privacy’ is taken up for better understanding the background of the interface. Tor, an anonymous communication network is referred in brief to state that the dilemma in the Privacy context is not unique to use of Crypto currencies technology in so far its negative effects are concerned . The conclusion suggest for finding an appropriate balance between an individual’s privacy and State’s security in Cryptocurrency technology.

The Pupillary Response to the Unknown: Novelty Versus Familiarity.

Object recognition is a type of perception that enables observers to recognize familiar shapes and categorize them into real-world identities. In this preregistered study, we aimed to determine whether pupil size changes occur during the perception and recognition of identifiable objects. We compared pupil size changes for familiar objects, nonobjects, and random noise. Nonobjects and noise produced greater pupil dilation than familiar objects. Contrary to previous evidence showing greater pupil dilation to stimuli with more perceptual and affective content, these results indicate a greater pupil dilation to stimuli that are unidentifiable. This is consistent with the relative salience of novelty compared to familiarity at the physiological level driving the pupil response.

A double-identity rumor spreading model

With the rapid spread of the Internet, the transmission of rumors via the electronic media has become ubiquitous. In the present paper, a double-identity rumor spreading model is established by considering the rumor spreading identities and real-world identities of the rumor creator, the rumor controller and the normal user. The diversity of these real-world identities encompasses five different attributes and the transition process between different rumor spreading identities. A new forgetting mechanism is proposed through a consideration of the influence of the interaction behavior on the new forgetting mechanism. Then, based on the simulation experiments of the proposed model, the impacts of the forgetting mechanism and the time lag between rumor creator and rumor controller on the spreading process of rumor are discussed. The results show that both elements play important roles in the dynamics of rumor propagation.

White-Box Implementation of Shamir’s Identity-Based Signature Scheme

Digital signature schemes have been extensively studied in the literature, where a large number of such schemes with different properties have been designed for different applications. For example, identity-based signature (IBS) schemes can efficiently map a user’s digital public key to his/her real-world identity (e.g., e-mail address). However, existing implementations of IBS schemes are not generally designed for white-box security (WBS), particularly concerning the protection of the private key when special attackers have full access to the execution environment. Therefore, in this paper, we propose the first white-box implementation for the classical Shamir’s IBS scheme. The basic idea is to utilize a mathematical transformation for embedding private key into some special tables, such that the original private key could be “invisible” during the execution process. We then analyze the security requirements achieved in our implementation, including the conventional black-box security under the random oracle model and WBS (e.g., key recovery attack resilience). This is the first IBS scheme implementation that satisfies WBS. It is also shown from the simulation that the implementation incurs a constant computational cost, which is realistic in deployments where a high security level is required.