Relevant bibliographies by topics / Anonymizing network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Anonymizing network'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Anonymizing network"

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Das, Sudipto, Omer Egecioglu, and Amr El Abbadi. "Anónimos: An LP-Based Approach for Anonymizing Weighted Social Network Graphs." IEEE Transactions on Knowledge and Data Engineering 24, no. 4 (April 2012): 590–604. http://dx.doi.org/10.1109/tkde.2010.267.

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Siddula, Madhuri, Yingshu Li, Xiuzhen Cheng, Zhi Tian, and Zhipeng Cai. "Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users." Wireless Communications and Mobile Computing 2020 (September 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8892321.

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While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.
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Moreno-Sanchez, Pedro, Tim Ruffing, and Aniket Kate. "PathShuffle: Credit Mixing and Anonymous Payments for Ripple." Proceedings on Privacy Enhancing Technologies 2017, no. 3 (July 1, 2017): 110–29. http://dx.doi.org/10.1515/popets-2017-0031.

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Abstract The I owe you (IOU) credit network Ripple is one of the most prominent alternatives in the burgeoning field of decentralized payment systems. Ripple’s path-based transactions set it apart from cryptocurrencies such as Bitcoin. Its pseudonymous nature, while still maintaining some regulatory capabilities, has motivated several financial institutions across the world to use Ripple for processing their daily transactions. Nevertheless, with its public ledger, a credit network such as Ripple is no different from a cryptocurrency in terms of weak privacy; recent demonstrative deanonymization attacks raise important concerns regarding the privacy of the Ripple users and their transactions. However, unlike for cryptocurrencies, there is no known privacy solution compatible with the existing credit networks such as Ripple. In this paper, we present PathShuffle, the first path mixing protocol for credit networks. PathShuffle is fully compatible with the current credit networks. As its essential building block, we propose PathJoin, a novel protocol to perform atomic transactions in credit networks. Using PathJoin and the P2P mixing protocol DiceMix, PathShuffle is a decentralized solution for anonymizing path-based transactions. We demonstrate the practicality of PathShuffle by performing path mixing in Ripple.
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Naumov, A. I., V. I. Radygin, and M. N. Ivanov. "IDENTIFICATION OF MIXER TRANSACTIONS IN THE BITCOIN NETWORK IN THE FRAMEWORK OF SOLVING THE PROBLEMS OF PREVENTING MONEY LAUNDERING AND TERRORIST FINANCING." SOFT MEASUREMENTS AND COMPUTING 1, no. 2 (2021): 78–90. http://dx.doi.org/10.36871/2618-9976.2021.02.007.

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This article addresses the problem of using cryptocurrencies in crimes related to money laundering and terrorist financing. The development of technologies that allow anonymizing participants of various cryptosystems not only increases their reliability and security for ordinary users, but also exposes such systems to the risk of being used by criminals, and also significantly complicates countering fraudulent or other illegal actions committed using cryptocurrencies. The authors propose algorithms that allow us to classify bitcoin transactions on the subject of whether they use mixers – the main means of hiding traces in the public blockchain and, perhaps, the main participant in most criminal schemes, whether it is money laundering or terrorist financing.
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Nastuła, Anna. "New threats in the cyberspace based on the analysis of the TOR (The Onion Router) network." ASEJ Scientific Journal of Bielsko-Biala School of Finance and Law 22, no. 4 (January 23, 2019): 28–31. http://dx.doi.org/10.5604/01.3001.0012.9839.

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New technologies change the society and generate new threats with respect to hiding human identity on the Internet. The aim of the paper is to present the origins, functioning and actual application of the TOR (The Onion Router) network. The author discusses the issue of anonymity as a driving force behind the TOR network which was ignited by social and political problems on the international arena. Also the relevance of TOR for the free flow of information and freedom of social communication on the Internet as well as thematic cross-section of its resources are presented. Further on in the paper the author shows a payment system in the TOR network which is based on cryptocurrencies, mainly on Bitcoin, and proves to be an ideal means of payment for illegal transactions. The paper also lists the most frequently committed crimes now and warns that the newly emerging forms of crimes which rely on anonymizing technologies, will pose a real challenge for law enforcement agencies and the system of justice. The TOR network by providing criminals with anonymity speeds up the development of cyber crime on an unprecedented scale and transfers traditional crime into a completely new dimension. The TOR network may have been well-meaning originally but has become a global threat.
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Martyniuk, Hanna, Valeriy Kozlovskiy, Serhii Lazarenko, and Yuriy Balanyuk. "Data Mining Technics and Cyber Hygiene Behaviors in Social Media." South Florida Journal of Development 2, no. 2 (May 26, 2021): 2503–15. http://dx.doi.org/10.46932/sfjdv2n2-108.

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The authors present in this work information about social media and data mining usage for that. It is represented information about social networking sites, where Facebook dominates the industry by boasting an account of 85% of the internet user’s worldwide. Applying data mining techniques to large social media data sets has the potential to continue to improve search results for everyday search engines, realize specialized target marketing for businesses, help psychologist study behavior, provide new insights into social structure for sociologists, personalize web services for consumers, and even help detect and prevent spam for all of us. The most common data mining applications related to social networking sites is represented. Authors have also gave information about different data mining techniques and list of these techniques. It is important to protect personal privacy when working with social network data. Recent publications highlight the need to protect privacy as it has been shown that even anonymizing this type of data can still reveal personal information when advanced data analysis techniques are used. A whole range of different threat of social networks is represented. Authors explain cyber hygiene behaviors in social networks, such as backing up data, identity theft and online behavior.
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Psaroudakis, Ioannis, Vasilios Katos, and Pavlos S. Efraimidis. "A novel mechanism for anonymizing Global System for Mobile Communications calls using a resource-based Session Initiation Protocol community network." Security and Communication Networks 8, no. 3 (June 26, 2014): 486–500. http://dx.doi.org/10.1002/sec.995.

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Franchi, Enrico, Agostino Poggi, and Michele Tomaiuolo. "Blogracy." International Journal of Distributed Systems and Technologies 7, no. 2 (April 2016): 37–56. http://dx.doi.org/10.4018/ijdst.2016040103.

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The current approach to build social networking systems is to create huge centralized systems owned by a single company. However, such strategy has many drawbacks, e.g., lack of privacy, lack of anonymity, risks of censorship and operating costs. In this paper the authors propose a novel P2P system that leverages existing, widespread and stable technologies such as DHTs and BitTorrent. In particular, they introduce a key-based identity system and a model of social relations for distributing content efficiently among interested readers. The system they propose, Blogracy, is a micro-blogging social networking system focused on: (i) anonymity and resilience to censorship; (ii) authenticatable content; (iii) semantic interoperability using activity streams. The authors have implemented the system and conducted various experiments to study its behaviour. The results are presented here, regarding (i) communication delays for some simulations of node churn, (ii) delays measured in test operations over PlanetLab, in direct communication, and (iii) through the I2P anonymizing network.
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Qiu, Ying, Yi Liu, Xuan Li, and Jiahui Chen. "A Novel Location Privacy-Preserving Approach Based on Blockchain." Sensors 20, no. 12 (June 21, 2020): 3519. http://dx.doi.org/10.3390/s20123519.

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Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.
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Zhou, Fan, Kunpeng Zhang, Shuying Xie, and Xucheng Luo. "Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach." INFORMS Journal on Computing 32, no. 3 (July 2020): 714–29. http://dx.doi.org/10.1287/ijoc.2019.0911.

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Cross-site account correlation correlates users who have multiple accounts but the same identity across online social networks (OSNs). Being able to identify cross-site users is important for a variety of applications in social networks, security, and electronic commerce, such as social link prediction and cross-domain recommendation. Because of either heterogeneous characteristics of platforms or some unobserved but intrinsic individual factors, the same individuals are likely to behave differently across OSNs, which accordingly causes many challenges for correlating accounts. Traditionally, account correlation is measured by analyzing user-generated content, such as writing style, rules of naming user accounts, or some existing metadata (e.g., account profile, account historical activities). Accounts can be correlated by de-anonymizing user behaviors, which is sometimes infeasible since such data are not often available. In this work, we propose a method, called ACCount eMbedding (ACCM), to go beyond text data and leverage semantics of network structures, a possibility that has not been well explored so far. ACCM aims to correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks. It models and understands latent representations of accounts using an embedding framework similar to sequences of words in natural language models. It also learns a transformation matrix to project node representations into a common dimensional space for comparison. With evaluations on both real-world and synthetic data sets, we empirically demonstrate that ACCM provides performance improvement compared with several state-of-the-art baselines in correlating user accounts between OSNs.
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Dissertations / Theses on the topic "Anonymizing network"

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Freedman, Michael J. (Michael Joseph) 1979. "A peer-to-peer anonymizing network layer." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87212.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaves 51-53).
by Michale J. Freedman.
M.Eng.
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Amati, Michele. "Design and implementation of an anonymous peer-to-peer iaas cloud." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8426/.

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Cloud services are becoming ever more important for everyone's life. Cloud storage? Web mails? Yes, we don't need to be working in big IT companies to be surrounded by cloud services. Another thing that's growing in importance, or at least that should be considered ever more important, is the concept of privacy. The more we rely on services of which we know close to nothing about, the more we should be worried about our privacy. In this work, I will analyze a prototype software based on a peer to peer architecture for the offering of cloud services, to see if it's possible to make it completely anonymous, meaning that not only the users using it will be anonymous, but also the Peers composing it will not know the real identity of each others. To make it possible, I will make use of anonymizing networks like Tor. I will start by studying the state of art of Cloud Computing, by looking at some real example, followed by analyzing the architecture of the prototype, trying to expose the differences between its distributed nature and the somehow centralized solutions offered by the famous vendors. After that, I will get as deep as possible into the working principle of the anonymizing networks, because they are not something that can just be 'applied' mindlessly. Some de-anonymizing techniques are very subtle so things must be studied carefully. I will then implement the required changes, and test the new anonymized prototype to see how its performances differ from those of the standard one. The prototype will be run on many machines, orchestrated by a tester script that will automatically start, stop and do all the required API calls. As to where to find all these machines, I will make use of Amazon EC2 cloud services and their on-demand instances.
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Gaertner, Jared Glen. "Anonymizing subsets of social networks." Thesis, 2012. http://hdl.handle.net/1828/4157.

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In recent years, concerns of privacy have become more prominent for social networks. Anonymizing a graph meaningfully is a challenging problem, as the original graph properties must be preserved as well as possible. We introduce a generalization of the degree anonymization problem posed by Liu and Terzi. In this problem, our goal is to anonymize a given subset of vertices in a graph while adding the fewest possible number of edges. We examine different approaches to solving the problem, one of which finds a degree-constrained subgraph to determine which edges to add within the given subset and another that uses a greedy approach that is not optimal, but is more efficient in space and time. The main contribution of this thesis is an efficient algorithm for this problem by exploring its connection with the degree-constrained subgraph problem. Our experimental results show that our algorithms perform very well on many instances of social network data.
Graduate
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Ferreira, Francisco Martins. "Anonymizing Private Information: From Noise to Data." Master's thesis, 2021. http://hdl.handle.net/10316/95554.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia
In the Information Age data has become more important for all types of organizations. The information carried by large datasets habilitates the creation of intelligent systems that overcome inefficiencies and create a safer and better quality of life. Because of this, organizations have come to see data as a competitive advantage.Fraud Detection solutions are one example of intelligent systems that are highly dependent on having access to large amounts of data. These solutions receive information about monetary transactions and classify them as legitimate or fraudulent in real time. This field has benefitted from higher availability of data, allowing the application of Machine Learning (ML) algorithms that leverage the information in datasets to finding fraudulent activity in real-time.In a context of systematic gathering of information, privacy dictates how data can be used and shared, in order to protect the information of users and organizations. In order to retain the utility of data, a growing amount of effort has been dedicated to creating and exploring avenues for privacy conscious data sharing.Generating synthetic datasets that carry the same information as real data allows for the creation of ML solutions while respecting the limitations placed on data usage. In this work, we introduce Duo-GAN and DW-GAN as frameworks for synthetic data generation that learn the specificities of financial transactions data and generate fictitious data that keeps the utility of the original collections of data. Both these frameworks use two generators, one for generating fraudulent instances and one for generating legitimate instances. This allows each generator to learn the distribution for each class, avoiding the problems created by highly unbalanced data. Duo-GAN achieves positive results, in some instances achieving a disparity of only 4% in F1 score between classifiers trained with synthetic data and classifiers trained with real data and both tested on the same real data. DW-GAN presents positive results too with disparity of 3% in F1 score in the same conditions.
Na Idade da Informação os dados tornaram-se mais importantes para todos os tipos de organizações. A informação contida pelos grandes datasets permite a criação de sistemas inteligentes que ultrapassam ineficiências e criam qualidade de vida melhor e mais segura. Devido a isto, as organizações começaram a ver os dados com uma vantagem competitiva.As soluções de Deteção de Fraude são exemplos de sistemas inteligentes que dependem do acesso a grandes quantidades de dados. Estas soluções recebem informação relativas a transações monetárias e atribuem classificações de legítimas ou fraudulentas em tempo real. Este é um dos campos que beneficiou da maior disponibilidade de dados, sendo capaz de aplicar algoritmos de Machine Learning que utilizam a informação contida nos datasets para detetar atividade fraudulenta em tempo real.Num contexto de agregação sistemática de informação, a privacidade dita como os dados podem ser utilizados e partilhados, com o objetivo de proteger a informação dos utilizadores de sistemas e de organizações. De forma a reter a utilidade dos dados, uma quantidade crescente de esforço tem sido dispendido em criar e explorar avenidas para a partilha de dados respeitando a privacidade.A geração de dados sintéticos que contém a mesma informação que os dados reais permite a criação de soluções de Machine Learning (ML) mantendo o respeito pelas limitações colocadas sobre a utilização de dados.Neste trabalho introduzimos Duo-GAN e DW-GAN como frameworks para geração de dados sintéticos que aprendem as especificidades dos dados de transações financeiras e geram dados fictícios que retém a utilidade das coleções de dados originais. Ambos os frameworks utilizam dois geradores, um para gerar instâncias fraudulentas e outro para gerar instâncias legítimas. Isto permite que cada gerador aprenda a distribuição de cada uma das classes, evitando assim os problemas criados por datasets desiquilibrados. O Duo- GAN atinge resultados positivos, em certos casos atingindo uma disparidade de apenas 4% no F1 score entre classificadores treinados com dados sintéticos e classificadores treinados com dados reais, e ambos testados nos mesmos dados reais. O DW-GAN também apresenta resultados positivos, com disparidade de 3% no F1 score para as mesmas condições.
Outro - This work is partially funded by national funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020 and by European Social Fund, through the Regional Operational Program Centro 2020. and by the CMU|Portugal project CAMELOT (POCI-01-0247-FEDER-045915).
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Book chapters on the topic "Anonymizing network"

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Wang, Shyue-Liang, Zheng-Ze Tsai, Tzung-Pei Hong, and I.-Hsien Ting. "Anonymizing Shortest Paths on Social Network Graphs." In Intelligent Information and Database Systems, 129–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20039-7_13.

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Freedman, Michael J., Emil Sit, Josh Cates, and Robert Morris. "Introducing Tarzan, a Peer-to-Peer Anonymizing Network Layer." In Peer-to-Peer Systems, 121–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45748-8_12.

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Fung, Benjamin C. M., Yan’an Jin, Jiaming Li, and Junqiang Liu. "Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining." In Lecture Notes in Social Networks, 77–100. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14379-8_5.

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Liu, Xiangyu, and Xiaochun Yang. "A Generalization Based Approach for Anonymizing Weighted Social Network Graphs." In Web-Age Information Management, 118–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23535-1_12.

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Psaroudakis, Ioannis, Vasilios Katos, and Pavlos S. Efraimidis. "A Framework for Anonymizing GSM Calls over a Smartphone VoIP Network." In IFIP Advances in Information and Communication Technology, 543–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30436-1_46.

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Lan, Lihui, and Biao Cong. "Weighted Social Networks Anonymizing Publication." In Recent Advances in Computer Science and Information Engineering, 413–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25781-0_63.

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Piacentino, Esteban, and Cecilio Angulo. "Anonymizing Personal Images Using Generative Adversarial Networks." In Bioinformatics and Biomedical Engineering, 395–405. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45385-5_35.

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Liu, Xiangyu, Jiajia Li, Dahai Zhou, Yunzhe An, and Xiufeng Xia. "Preserving the d-Reachability When Anonymizing Social Networks." In Web-Age Information Management, 40–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39958-4_4.

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Zhang, Hongyan, Li Xu, Limei Lin, and Xiaoding Wang. "De-anonymizing Social Networks with Edge-Neighborhood Graph Attacks." In Communications in Computer and Information Science, 726–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9129-7_49.

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Wang, Huanjie, Peng Liu, Shan Lin, and Xianxian Li. "A Local-Perturbation Anonymizing Approach to Preserving Community Structure in Released Social Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 36–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60717-7_4.

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Conference papers on the topic "Anonymizing network"

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Das, Sudipto, Omer Egecioglu, and Amr El Abbadi. "Anonymizing weighted social network graphs." In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icde.2010.5447915.

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Masoumzadeh, Amirreza, and James Joshi. "Anonymizing geo-social network datasets." In the 4th ACM SIGSPATIAL International Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2071880.2071886.

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Kayem, Anne V. D. M., Azhar Deshai, and Stuart Hammer. "On anonymizing social network graphs." In 2012 Information Security for South Africa (ISSA). IEEE, 2012. http://dx.doi.org/10.1109/issa.2012.6320456.

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Lan, Lihui, Shiguang Ju, and Hua Jin. "Anonymizing Social Network Using Bipartite Graph." In 2010 International Conference on Computational and Information Sciences (ICCIS). IEEE, 2010. http://dx.doi.org/10.1109/iccis.2010.245.

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Song, Wenlue, Yan Zhang, and Wenyang Bai. "Anonymizing Path Nodes in Social Network." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5658931.

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Qardaji, Wahbeh, and Ninghui Li. "Anonymizing Network Traces with Temporal Pseudonym Consistency." In 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCS Workshops). IEEE, 2012. http://dx.doi.org/10.1109/icdcsw.2012.11.

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Tang, Yi, and Yuanyuan Wu. "Anonymizing Network Addresses Based on Clustering Subnets." In 2010 International Conference on Internet Technology and Applications (iTAP). IEEE, 2010. http://dx.doi.org/10.1109/itapp.2010.5566245.

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Coull, Scott E., Fabian Monrose, Michael K. Reiter, and Michael Bailey. "The Challenges of Effectively Anonymizing Network Data." In Technology Conference for Homeland Security (CATCH). IEEE, 2009. http://dx.doi.org/10.1109/catch.2009.27.

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Han, Jinsong, and Yunhao Liu. "Rumor Riding: Anonymizing Unstructured Peer-to-Peer Systems." In 2006 IEEE International Conference on Network Protocols. IEEE, 2006. http://dx.doi.org/10.1109/icnp.2006.320195.

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Yi Tang, Yuanyuan Wu, and Quan Zhou. "AASC: Anonymizing network addresses based on subnet clustering." In 2010 IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS). IEEE, 2010. http://dx.doi.org/10.1109/wcins.2010.5541864.

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Reports on the topic "Anonymizing network"

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Moskowitz, Ira S., Daniel P. Crepeau, Richard E. Newman, and Allen R. Miller. Covert Channels and Anonymizing Networks. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada465268.

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Moskowitz, Ira S., Richard E. Newman, Daniel P. Crepeau, and Allen R. Miller. A Detailed Mathematical Analysis of a Class of Covert Channels Arising in Certain Anonymizing Networks. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada417139.

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