Relevant bibliographies by topics / K-Anonymisation

Contents

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

Academic literature on the topic 'K-Anonymisation'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "K-Anonymisation"

1

Loukides, Grigorios, and Jian-Hua Shao. "An Efficient Clustering Algorithm for k-Anonymisation." Journal of Computer Science and Technology 23, no. 2 (2008): 188–202. http://dx.doi.org/10.1007/s11390-008-9121-3.

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Natwichai, Juggapong, Xue Li, and Asanee Kawtrkul. "Incremental processing and indexing for (k, e)-anonymisation." International Journal of Information and Computer Security 5, no. 3 (2013): 151. http://dx.doi.org/10.1504/ijics.2013.055836.

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Stark, Konrad, Johann Eder, and Kurt Zatloukal. "Achieving k-anonymity in DataMarts used for gene expressions exploitation." Journal of Integrative Bioinformatics 4, no. 1 (2007): 132–44. http://dx.doi.org/10.1515/jib-2007-58.

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Abstract Gene expression profiling is a sophisticated method to discover differences in activation patterns of genes between different patient collectives. By reasonably defining patient groups from a medical point of view, subsequent gene expression analysis may reveal disease-related gene expression patterns that are applicable for tumor markers and pharmacological target identification. When releasing patient-specific data for medical studies privacy protection has to be guaranteed for ethical and legal reasons. k-anonymisation may be used to generate a sufficient number of k data twins in
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4

de Haro-Olmo, Francisco José, Ángel Jesús Varela-Vaca, and José Antonio Álvarez-Bermejo. "Blockchain from the Perspective of Privacy and Anonymisation: A Systematic Literature Review." Sensors 20, no. 24 (2020): 7171. http://dx.doi.org/10.3390/s20247171.

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The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption,
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Zhang, Yuliang, Tinghuai Ma, Jie Cao, and Meili Tang. "K-anonymisation of social network by vertex and edge modification." International Journal of Embedded Systems 8, no. 2/3 (2016): 206. http://dx.doi.org/10.1504/ijes.2016.076114.

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Sahil bucha. "Balancing Privacy and Utility: Anonymisation Techniques for E-commerce Logistics Data." International Journal of Engineering Research and Science & Technology 17, no. 2 (2021): 65–75. https://doi.org/10.62643/ijerst.v17n2pp.65-75.

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The study analyses the application of anonymisation techniques in e-commerce logistics, focusing on methods such as differential privacy, k-anonymity, and synthetic data generation. The growing concerns over data privacy have prompted organisations in the matter of adopting techniques to protect the sensitive information of customers while maintaining data utility for analytical purposes. The case studies highlight real-world applications, like the use of differential privacy to enhance delivery route optimisation, synthetic data to improve inventory forecasting, and k-anonymity for protecting
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Ganabathi, G. Chitra, and P. Uma Maheswari. "Efficient clustering technique for k-anonymisation with aid of optimal KFCM." International Journal of Business Intelligence and Data Mining 15, no. 4 (2019): 430. http://dx.doi.org/10.1504/ijbidm.2019.102809.

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Singh, Amardeep, Monika Singh, Divya Bansal, and Sanjeev Sofat. "Optimised K-anonymisation technique to deal with mutual friends and degree attacks." International Journal of Information and Computer Security 14, no. 3/4 (2021): 281. http://dx.doi.org/10.1504/ijics.2021.114706.

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Sofat, Sanjeev, Divya Bansal, Monika Singh, and Amardeep Singh. "Optimised K-anonymisation technique to deal with mutual friends and degree attacks." International Journal of Information and Computer Security 14, no. 3/4 (2021): 281. http://dx.doi.org/10.1504/ijics.2021.10037248.

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Yaji, Sharath, and B. Neelima. "Parallel computing for preserving privacy using k-anonymisation algorithms from big data." International Journal of Big Data Intelligence 5, no. 3 (2018): 191. http://dx.doi.org/10.1504/ijbdi.2018.092659.

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Dissertations / Theses on the topic "K-Anonymisation"

1

Mauger, Clémence. "Optimisation de l'utilité des données lors d'un processus de k-anonymisation." Electronic Thesis or Diss., Amiens, 2021. http://www.theses.fr/2021AMIE0076.

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Pour donner des garanties de protection de la vie privée aux bases de données anonymisées, des modèles d'anonymisation ont vu le jour ces dernières décennies. Parmi ceux-ci, on peut citer la k-anonymité, la l-diversité, la t-proximité ou encore la confidentialité différentielle. Dans cette thèse, je me suis intéressée au modèle de k-anonymité à travers une analyse approfondie des manières de produire des bases remplissant ces critères de confidentialité tout en optimisant l'utilité des données. Partant d'une base de données, on peut en effet construire plusieurs versions k-anonymes de cette ba
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2

Verster, Cornelis Thomas. "On supporting K-anonymisation and L-diversity of crime databases with genetic algorithms in a resource constrained environment." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/20016.

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The social benefits derived from analysing crime data need to be weighed against issues relating to privacy loss. To facilitate such analysis of crime data Burke and Kayem [7] proposed a framework (MCRF) to enable mobile crime reporting in a developing country. Here crimes are reported via mobile phones and stored in a database owned by a law enforcement agency. The expertise required to perform analysis on the crime data is however unlikely to be available within the law enforcement agency. Burke and Kayem [7] proposed anonymising the data(using manual input parameters) at the law enforcement
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Sondeck, Louis-Philippe. "Privacy and utility assessment within statistical data bases." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0023/document.

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Les données personnelles sont d’une importance avérée pour presque tous les secteurs d’activité économiques grâce à toute la connaissance qu’on peut en extraire. Pour preuve, les plus grandes entreprises du monde que sont: Google, Amazon, Facebook et Apple s’en servent principalement pour fournir de leurs services. Cependant, bien que les données personnelles soient d’une grande utilité pour l’amélioration et le développement de nouveaux services, elles peuvent aussi, de manière intentionnelle ou non, nuire à la vie privée des personnes concernées. En effet, plusieurs études font état d’attaqu
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Sondeck, Louis-Philippe. "Privacy and utility assessment within statistical data bases." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0023.

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Les données personnelles sont d’une importance avérée pour presque tous les secteurs d’activité économiques grâce à toute la connaissance qu’on peut en extraire. Pour preuve, les plus grandes entreprises du monde que sont: Google, Amazon, Facebook et Apple s’en servent principalement pour fournir de leurs services. Cependant, bien que les données personnelles soient d’une grande utilité pour l’amélioration et le développement de nouveaux services, elles peuvent aussi, de manière intentionnelle ou non, nuire à la vie privée des personnes concernées. En effet, plusieurs études font état d’attaqu
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Book chapters on the topic "K-Anonymisation"

1

Loukides, Grigorios, Achilles Tziatzios, and Jianhua Shao. "Towards Preference-Constrained k-Anonymisation." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04205-8_20.

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"Graph Modification Approaches." In Security, Privacy, and Anonymization in Social Networks. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5158-4.ch005.

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This chapter contains some of the most recent techniques and algorithms on social network anonymisation. The authors start with the random perturbation algorithms like the UMGA algorithm and constrained perturbation algorithms like the fast k-degree anonymization (FKDA) algorithm. Then they move to the anonymisation technique, noise nodes addition, and present an algorithm based upon this approach. Next, the authors move on to α-anonymization, (α, k) anonymity, (α, l) diversity, and recursive (α, c, l) diversity anonymisation algorithms, which are generalisations in that order.
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Conference papers on the topic "K-Anonymisation"

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Zemanek, Vit, Yixin Hu, Pepijn De Reus, Ana Oprescu, and Ivano Malavolta. "Exploring the Impact of K-Anonymisation on the Energy Efficiency of Machine Learning Algorithms." In 2024 10th International Conference on ICT for Sustainability (ICT4S). IEEE, 2024. https://doi.org/10.1109/ict4s64576.2024.00022.

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Loukides, Grigorios, and Jianhua Shao. "Capturing data usefulness and privacy protection in K-anonymisation." In the 2007 ACM symposium. ACM Press, 2007. http://dx.doi.org/10.1145/1244002.1244091.

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Loukides, Grigorios, and Jianhua Shao. "Greedy Clustering with Sample-Based Heuristics for K-Anonymisation." In The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007). IEEE, 2007. http://dx.doi.org/10.1109/isdpe.2007.102.

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Loukides, Grigorios, and Jianhua Shao. "Towards Balancing Data Usefulness and Privacy Protection in K-Anonymisation." In The Sixth IEEE International Conference on Computer and Information Technology (CIT'06). IEEE, 2006. http://dx.doi.org/10.1109/cit.2006.184.

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Loukides, Grigorios, and Jianhua Shao. "Data utility and privacy protection trade-off in k-anonymisation." In the 2008 international workshop. ACM Press, 2008. http://dx.doi.org/10.1145/1379287.1379296.

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Tripathy, B. K., and Anirban Mitra. "An algorithm to achieve k-anonymity and l-diversity anonymisation in social networks." In 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN). IEEE, 2012. http://dx.doi.org/10.1109/cason.2012.6412390.

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de Reus, Pepijn, Ana Oprescu, and Koen van Elsen. "Energy Cost and Machine Learning Accuracy Impact of k-Anonymisation and Synthetic Data Techniques." In 2023 International Conference on ICT for Sustainability (ICT4S). IEEE, 2023. http://dx.doi.org/10.1109/ict4s58814.2023.00015.

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Journal articles
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