Anonymity Features Research Articles

A privacy-enhanced framework with deep learning for botnet detection

A botnet is a group of hijacked devices that conduct various cyberattacks, which is one of the most dangerous threats on the internet. Organizations or individuals use network traffic to mine botnet communication behavior features. Network traffic often contains individual users’ private information, such as website passwords, personally identifiable information, and communication content. Among the existing botnet detection methods, whether they extract deterministic traffic interaction features, use DNS traffic, or methods based on raw traffic bytes, these methods focus on the detection performance of the detection model and ignore possible privacy leaks. And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. Therefore, preventing malicious persons from stealing data to infer privacy during the botnet detection process has become an issue worth pondering. Based on this problem, this article proposes a privacy-enhanced framework with deep learning for botnet detection. The goal of this framework is to learn a feature extractor. It can hide the private information that the attack model tries to infer from the intermediate anonymity features, while maximally retaining the interactive behavior features contained in the original traffic for botnet detection. We design a privacy confrontation algorithm based on a mutual information calculation mechanism. This algorithm simulates the game between the attacker trying to infer private information through the attack model and the data processor retaining the original content of the traffic to the maximum extent. In order to further ensure the privacy protection of the feature extractor during the training process, we train the feature extractor in the federated learning training mode. We extensively evaluate our approach, validating it on two public datasets and comparing it with existing methods. The results show that our method can effectively ensure detection accuracy on the basis of removing private information. For the CTU-13 dataset, the detection framework achieves the best detection performance; for the ISCX-2014 dataset, the accuracy of the framework is less than 1% lower than the best effect.

Telegram under Fire: Exposing the Problems with the Most Divisive Messaging App in the World

Telegram has been hailed for more than ten years as a safe haven for uncensored communication, where users may express themselves without the limitations and content control common on other well-known social networking sites. With its promise of unparalleled user privacy, encryption, and little censorship, Telegram, founded in 2013 by Russian-born businessman Pavel Durov, became a rival to industry titans like Facebook Messenger and WhatsApp. Telegram's story has taken a darker turn in recent years, despite its initial celebration as a symbol of digital freedom, especially in areas where state-imposed limitations impede free speech. In addition to facilitating lawful free expression, its lax moderation guidelines, anonymity features, and encryption capabilities have created an environment favorable for criminal activity, extremist propaganda, misinformation campaigns, and various other illegal endeavors.With more than 800 million users, Telegram's worldwide impact is undeniable. Its rapid, mostly unchecked growth has made it a vital instrument for communication and a breeding ground for harmful information. Governments, law enforcement organizations, cybersecurity specialists, and human rights groups are alarmed by this dichotomy. However, the platform's supporters point to its ability to support political opposition, facilitate emergency communication in crises, and provide an online lifeline free from government monitoring to residents under repressive regimes. As a result, the argument is about more than just messaging app regulation; it is a stand-in for more significant disputes about digital rights, security, and the direction of global information networks.

(Re) growing plant awareness: A Delphi study

Societal Impact StatementWithout a robust construct, it is currently difficult for researchers to systematically assess and facilitate a societal shift toward greater plant awareness. Using a Delphi study, we take a first step towards such construct by developing a definition and testable dimensions. This paper proposes a first expert validated definition and three distinct dimensions that allow researchers to assess plant awareness and to identify and compare key influences on the construct across different socio‐cultural groups. With this improved framework, research can support initiatives to promote plant awareness in society, a critical step in addressing challenges such as the climate crisis.Summary Research on plant blindness, plant awareness disparity, and plant awareness is currently separated using different terminologies. Having different terms for the same construct is disadvantageous for research, as ambiguous constructs can lead to important research being overlooked. In this study, we aim to provide a solution to this challenge by applying a Delphi study to integrate previous concepts into the new comprehensive construct of plant awareness. Delphi is a state‐of‐the‐art method for reaching reliable expert consensus on multidisciplinary issues with its key features of anonymity, iterative questioning and feedback. Focusing on the methodology, we provide a transparent, and detailed, overview of how we applied this method to reach consensus on the plant awareness construct. As an outcome of our Delphi study, we provide an expert validated definition of plant awareness that is based on previous constructs, research, and expert consensus. This definition constitutes three key dimensions: attention, understanding, and attitudes. Experts found consensus on these three dimensions, their definition, and how increased plant awareness is expressed in relation to each of the dimensions. Finally, we identified several factors that are key to raising plant awareness. This validated definition alongside the differentiation of the three dimensions not only assists comprehension of previous research but for the first time opens the possibility of developing a valid and reliable tool to assess an individual's plant awareness. Therefore, this study significantly contributes to promoting research on plant awareness and consequently, towards fostering plant awareness as a common global goal.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

The Role of Information Governance in Mitigating Financial Crime Risks in Stablecoin Transactions

This study investigates the role of information governance in mitigating financial crime risks in stablecoin transactions. Using a variety of analytical techniques, including simple linear regression, sentiment analysis with NLP tools, logistic regression, and machine learning models, the research evaluates the impact of information governance on innovation, user trust, financial crimes, and the effectiveness of combined compliance measures. The findings indicate that strict information governance regulations reduce innovation but enhance market stability and user trust. Robust governance correlates strongly with increased user adoption, while higher anonymity features in stablecoins are linked to a higher incidence of financial crimes. Integrating KYC/AML compliance with transaction monitoring significantly improves the detection and prevention of financial crimes compared to standalone approaches. These insights provide valuable guidance for policymakers, regulatory authorities, and financial institutions to develop strategies that balance innovation with enhanced security and compliance in the stablecoin market.

Edukasi Bullying dan Bahaya Penggunaan Gadget Melalui Program Smart pada Remaja

Cyberbullying is an action intended to embarrass, intimidate, spread ugliness and hatred in cyber media. One of the cyber media is social media ask.fm. Cyberbullying often occurs in teenagers, this can affect the emotions of teenagers who are still quite unstable in controlling their emotions. In this research, the problem formulated is whether there is an influence of cyberbullying on social media ask.fm on the emotional development of teenagers. The aim of this research is to determine the effect of cyberbullying on ask.fm social media on adolescent emotional disorders. The research method used is a survey research method using a questionnaire as a data collection instrument. The catharsis theory used in this research states that catharsis is a clearing of emotional conflict within oneself through talking about it and the deindividuation theory states that there is a feeling of loss of personal identity and the emergence of anonymity in the group. The anonymous feature on ask.fm social media provides opportunities for perpetrators to carry out cyberbullying on ask.fm. The sample in this study was 30 students, who had been victims and perpetrators of cyberbullying. Hypothesis testing shows Tcount ≥ Ttable (3.049 ≥ 1.660) so that H0 is rejected and Ha is accepted, so this means that these two variables have a significant influence. This research showed that teenagers dared to carry out attacks only through the social media ask.fm, with anonymous facilities that made it easy for the perpetrators to express all their frustration without their identities being known. And as victims, teenagers generally reject it by responding emotionally.

COMPLEX ANALYSIS OF CRYPTOCURRENCIES AND THEIR IMPLICATIONS IN THE CONTEXT OF MONEY LAUNDERING AND TERRORISM FINANCING

This study investigates the potential role of Bitcoin in terrorism financing by analyzing the decentralization and anonymity features of cryptocurrencies that facilitate illegal transactions. In the context of delayed regulatory capacity regarding innovations in financial technology, the study explores new opportunities for terrorism financing. International reports, such as that of the financial action task force on money laundering, highlight the growing risks of terrorism financing associated with virtual currencies like Bitcoin. While terrorist financing based on emerging technologies enables terrorist organizations to swiftly transfer funds globally, this phenomenon simultaneously increases the difficulties in combating terrorism financing at the national level. In light of this technological revolution and the expansion of terrorism, the study focuses on the interaction between Bitcoin and terrorism, exploring whether terrorist attacks significantly contribute to Bitcoin price fluctuations and to what extent the cryptocurrency may evolve into a currency used for terrorism financing.

Towards post-quantum authenticated key agreement scheme for mobile devices

Mobile device communication enables mobile devices to connect with each other, exchange data, and access services, where security is a crucial aspect to protect sensitive data and privacy. The authenticated key agreement (AKA) protocols ensure authorized and secure communication in such environments. As the security of current AKA protocols relies on the hardness of discrete logarithmic or factorization, it will lead to a greater threat in the post-quantum world. Consequently, Ding et al. created a lattice-based AKA scheme for the quantum world. Although the protocol is simple for mobile devices, it is susceptible to insider attacks. Moreover, the scope of efficiency enhancement of this protocol needs to be explored. Therefore, to provide a secure and efficient solution, a new lattice-based AKA scheme has been proposed in this paper. It is designed with the idea of zero knowledge-based authentication. Further, our protocol resists key mismatch, and signal leakage attacks and supports the reuse of the private key of the server, perfect forward secrecy, and anonymity features. Additionally, we have talked about how our protocol stacks up in terms of computing complexity when compared to competing with other zero knowledge-based authenticated key agreement systems.

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.

Reinforcing privacy in cloud computing via adaptively secure non-zero inner product encryption and anonymous identity-based revocation in unbounded setting

Cloud computing serves as an advanced computing technology to support Internet services and has numerous applications, including medical fields, online data storage, social network, big data analysis, and e-learning platforms. Inner product encryption (IPE) is a promising cryptographic primitive that facilitates access control over outsourced encrypted data by restricting decryption capability via an inner product relation. Most of the existing IPEs are built assuming a priori bound on the length of attribute vectors associated with the plane data. A situation may occur when adding new attributes is essential due to upgradation. Thus it is desirable to construct IPE with variable length attribute vectors instead of fixing the length at the setup phase. Such IPEs are more flexible while encrypting data and consequently have more real-life applications. In the following, we make our contributions:–We design the first efficient adaptively payload-hiding secure unbounded non-zero IPE (UNIPE) scheme from the standard symmetric external Diffie-Hellman (SXDH) assumption and prove security in the standard model.–We propose a generic construction of an adaptively weak attribute-hidingUNIPE from an unbounded inner product functional encryption (UIPFE) scheme. We also instantiate UNIPE from the SXDH assumption and prove security in the standard model.–Finally, we present the first unbounded anonymous identity-based revocation (UAnon-IBRV) scheme using our generic UNIPE as the building block. The anonymity feature ensures that the ciphertext reveals no information about the revoking sets of the system.

Easier, faster and safer: The social organization of drug dealing through encrypted messaging apps

AbstractEncrypted messaging apps like Telegram have become an increasingly attractive tool for drug dealing. The combination of easy opening and closing of user communities, powerful anonymity features, and operability on smartphones is changing the way drugs are delivered at the end of the chain. However, much of the recent research on Internet‐based drug markets has focused on cryptomarkets, but also on social media and the clear web. Drawing on economic sociology literature on marketplaces and partial organizations, this article fills this gap by analyzing the social organization of drug economies based on messaging apps. We argue that this form of drug trade enhances the experience of both selling and consuming drugs. Based on a unique set of in‐depth interviews with drug sellers and users belonging to four different Telegram groups in Argentina, this article shows that Telegram communities offer a wider variety of substances, speed up drug delivery, create the perception that police detection is unlikely, and promote drug specialization among sellers. This research expands our understanding of how technology and specific organizational forms transform both drug provision and consumption.

Designing access control security protocol for Industry 4.0 using Blockchain‐as‐a‐Service

AbstractIndustry 4.0 is a revolution of the operations in the industrial manufacturing for increased productivity, trade and commerce. It is heavily reliant on the automation of the processes and equipment along with complex interconnectivity and insightful analysis using machine learning. The interconnectivity of the manufacturing devices from various industrial sites brings with it several security issues related to communication. This article focuses on solving the security issue of access control between such devices and enable seamless secure communication for the proper functioning of the industry. An access control scheme has been proposed that achieves the necessary security features of anonymity, traceability, and forward secrecy. It is also shown that the proposed scheme takes less communication and computational costs, and is strongly resilient against various attacks such as impersonation attack, replay attack, and denial‐of‐service attack as compared to other relevant schemes.

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.

Social Media based Hate Speech Detection using Machine Learning

Abstract: Hate speech is a crime that has been increasing in recent years, not only in person but also online. There are several causes for this. There is tremendous growth in social media that promotes full freedom of expression through anonymity features. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. It promotes violence and hate crimes, creates social imbalances, and undermines peace, trust and human rights. Revealing hate speech in social media discourse is a very important but complex task.

Semantic-Based Classification of Toxic Comments Using Ensemble Learning

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this.

Social Media based Hate Speech Detection using Machine Learning

Hate speech is a crime that has been increasing in recent years, not only in person but also online. There are several causes for this. There is tremendous growth in social media that promotes full freedom of expression through anonymity features. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. It promotes violence and hate crimes, creates social imbalances, and undermines peace, trust and human rights. Revealing hate speech in social media discourse is a very important but complex task. On the one hand, the anonymity provided by the Internet, especially social networks, makes people more likely to engage in hostile behavior. On the other hand, the desire to express one's thoughts on the Internet has increased, leading to the spread of hate speech. Governments and social media platforms can benefit from detection and prevention technologies, as this kind of bigoted language can wreak havoc on society. We help resolve this dilemma by providing a systematic overview of research on this topic in this survey. This project aims to accurately predict various forms by addressing different categories of hate individually and examining a set of text mining functions. Hate speech detection

Perceptions of the Senior High School Students on the Use of Classroom Response System in Learning Mathematics

Classroom Response System (CRS) is one of the educational tools teachers can use to promote active learning and improve student learning. Despite the promising benefits of the CRS, there is less account of its impact in secondary mathematics classrooms as compared to higher education. In this study, a descriptive survey design was used to describe the perception of Senior High School students on the use of the CRS in learning mathematics. Students were exposed to Blicker, a classroom response system tool that uses Bluetooth Low Energy to interact with the teacher. Survey shows that CRS increase students’ engagement, interactivity, and students perceived CRS to be beneficial for their learning. Moreover, students favored the CRS’ anonymity feature and enjoyed the affordability of the CRS to promote student-teacher interaction through the question-feedback process. The interview shared common themes on the CRS benefits and validated students’ claims that CRS attributed to an increase in engagement, interactivity, and learning in mathematics. Overall, the student’s perceptions of the use of CRS are overwhelmingly positive and students strongly recommend the use of CRS in other learning areas.

Managed anonymity of CBDC, social welfare and taxation: A new monetarist perspective

ABSTRACT China’s central bank digital currency (CBDC), e-CNY, is currently in a large-scale pilot stage. In this study, a new monetarist model, both theoretically and quantitively, is developed to assess the relationship between the managed anonymity feature of e-CNY, social welfare, and taxation. The findings are as follows. First, the introduction of managed anonymous CBDC affects the official and shadow economy by increasing the diversity of payment instruments and suppressing tax evasion, thereby improving social welfare and government tax revenue. Second, if CBDC is ‘cash-like’ in the sense that it offers relatively high anonymity, then issuing CBDC meets the public demand for anonymous small value payment services and enhances the individual welfare of most households. Third, if CBDC is ‘deposit-like’ in the sense that it offers relatively low anonymity, then issuing CBDC combats illegal transactions in the shadow economy, and increases the total amount of social welfare and government tax revenue. The model, calibrated to the Chinese economy, suggests that aggregate welfare and government tax revenue can be increased by up to 3.2% and 10%, respectively. These findings suggest that policy-makers can dynamically adjust the anonymity design of CBDC to better align it with changing policy objectives and economic conditions.

On the Example of a Newly Found Prose Ihtilac-name

Geleceğe dair bilgi almak için çeşitli falcılık metotları kullanmak, insanların yüzlerce yıldır sürdürdükleri bir eylemdir. Falcılığı anlatan metinler Türk edebiyatının ilk yazılı eserlerine kadar uzanır. Geleneğin bir ürünü olan seyirme veya ihtilâc sonucu gelecekten haber bekleme, çıkarımda bulunma olayı bu yönüyle anonim özellik taşırlar. Seğirmelerle ilgili çıkarımlar genelde Cafer-i Sadık, İskender, Danyal gibi isimlerle temellendirilir. Bu yazıda ele alınacak olan yazma da bir tür fal kitabı olan ihtilâc-nâme türündendir. İnsan vücudundaki seğirmelerden yola çıkarak, kişinin geleceğine dair yorumlar yapan bu tür eserler, müstakil bir kitap halinde ya da bir kitap bölümü olarak karşımıza çıkmaktadır. Bu çalışmada incelediğimiz eser, Ankara Üniversitesi İlahiyat Fakültesi Kütüphanesi Yazma Eserler Kataloğunda 1641 numarada kayıtlı olan mensur olarak kaleme alınmış bir ihtilâc-nâmedir. Bu eser içerdiği 118 uzva ait 129 seğirmeyle orta hacimli sayılabilecek bir ihtilâc-nâme örneğidir. Bu makalede eserin, Latin harfli metni ortaya konmaya çalışılacak ve yakın zamanda yapılmış bazı çalışmalarla içerik olarak karşılaştırılacaktır. Bunun yanında metinde dikkati çeken dil ve imla özelliklerine de değinilecektir. Makalenin sonuna da eserin yazma nüshası eklenecektir.

Privacy in the sharing economy: Why don't users disclose their negative experiences?

A consistent positivity bias has been found in many sharing economy platforms. Extreme positivity bias in reviews makes the review system meaningless, misleads the public, deteriorates users' trust in the sharing platform, and negatively influences consumers' participation on the platform. Considerable anecdotal evidence shows that privacy concerns are a barrier to providing honest negative reviews. However, research studies on the consumers' impact of privacy concerns on review behaviors are sparse. This study aims to fill the gap by investigating consumers' privacy concerns in writing negative reviews. Specifically, this study examines the effects of two types of privacy concerns, online privacy concerns and physical privacy concerns, on the intention to provide negative reviews and contingent factors (monetary incentive, venting negative feelings, and warning other consumers) that could influence this relationship. The study also examines the effectiveness of two fair information practices (i.e., privacy policy and anonymous feature) in mitigating privacy concerns. We evaluate our research model using a scenario-based online survey, providing broad support for our hypotheses. The study delineates the implications of the results for both practice and research.