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Статті в журналах з теми "Privacy preserving clustering"
Hegde, Aditya, Helen Möllering, Thomas Schneider, and Hossein Yalame. "SoK: Efficient Privacy-preserving Clustering." Proceedings on Privacy Enhancing Technologies 2021, no. 4 (July 23, 2021): 225–48. http://dx.doi.org/10.2478/popets-2021-0068.
Повний текст джерелаLyu, Lingjuan, James C. Bezdek, Yee Wei Law, Xuanli He, and Marimuthu Palaniswami. "Privacy-preserving collaborative fuzzy clustering." Data & Knowledge Engineering 116 (July 2018): 21–41. http://dx.doi.org/10.1016/j.datak.2018.05.002.
Повний текст джерелаGao, Zhiqiang, Yixiao Sun, Xiaolong Cui, Yutao Wang, Yanyu Duan, and Xu An Wang. "Privacy-Preserving Hybrid K-Means." International Journal of Data Warehousing and Mining 14, no. 2 (April 2018): 1–17. http://dx.doi.org/10.4018/ijdwm.2018040101.
Повний текст джерелаMohassel, Payman, Mike Rosulek, and Ni Trieu. "Practical Privacy-Preserving K-means Clustering." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 414–33. http://dx.doi.org/10.2478/popets-2020-0080.
Повний текст джерелаNi, Weiwei, and Zhihong Chong. "Clustering-oriented privacy-preserving data publishing." Knowledge-Based Systems 35 (November 2012): 264–70. http://dx.doi.org/10.1016/j.knosys.2012.05.012.
Повний текст джерелаJahan, Thanveer. "Privacy Preserving Clustering on Distorted data." IOSR Journal of Computer Engineering 5, no. 2 (2012): 25–29. http://dx.doi.org/10.9790/0661-0522529.
Повний текст джерелаWei, Weiming, Chunming Tang, and Yucheng Chen. "Efficient Privacy-Preserving K-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation." Entropy 24, no. 8 (August 18, 2022): 1145. http://dx.doi.org/10.3390/e24081145.
Повний текст джерелаOliveira, Stanley R. M., and Osmar R. Zaïane. "Privacy-Preserving Clustering to Uphold Business Collaboration." International Journal of Information Security and Privacy 1, no. 2 (April 2007): 13–36. http://dx.doi.org/10.4018/jisp.2007040102.
Повний текст джерелаNguyen, Huu Hiep. "Privacy-preserving mechanisms for k-modes clustering." Computers & Security 78 (September 2018): 60–75. http://dx.doi.org/10.1016/j.cose.2018.06.003.
Повний текст джерелаİnan, Ali, Selim V. Kaya, Yücel Saygın, Erkay Savaş, Ayça A. Hintoğlu, and Albert Levi. "Privacy preserving clustering on horizontally partitioned data." Data & Knowledge Engineering 63, no. 3 (December 2007): 646–66. http://dx.doi.org/10.1016/j.datak.2007.03.015.
Повний текст джерелаДисертації з теми "Privacy preserving clustering"
Cui, Yingjie, and 崔英杰. "A study on privacy-preserving clustering." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4357225X.
Повний текст джерелаCui, Yingjie. "A study on privacy-preserving clustering." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4357225X.
Повний текст джерелаThapa, Nirmal. "CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/15.
Повний текст джерелаLin, Zhenmin. "Privacy Preserving Distributed Data Mining." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/9.
Повний текст джерелаChatalic, Antoine. "Efficient and privacy-preserving compressive learning." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S030.
Повний текст джерелаThe topic of this Ph.D. thesis lies on the borderline between signal processing, statistics and computer science. It mainly focuses on compressive learning, a paradigm for large-scale machine learning in which the whole dataset is compressed down to a single vector of randomized generalized moments, called the sketch. An approximate solution of the learning task at hand is then estimated from this sketch, without using the initial data. This framework is by nature suited for learning from distributed collections or data streams, and has already been instantiated with success on several unsupervised learning tasks such as k-means clustering, density fitting using Gaussian mixture models, or principal component analysis. We improve this framework in multiple directions. First, it is shown that perturbing the sketch with additive noise is sufficient to derive (differential) privacy guarantees. Sharp bounds on the noise level required to obtain a given privacy level are provided, and the proposed method is shown empirically to compare favourably with state-of-the-art techniques. Then, the compression scheme is modified to leverage structured random matrices, which reduce the computational cost of the framework and make it possible to learn on high-dimensional data. Lastly, we introduce a new algorithm based on message passing techniques to learn from the sketch for the k-means clustering problem. These contributions open the way for a broader application of the framework
Chen, Keke. "Geometric Methods for Mining Large and Possibly Private Datasets." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11561.
Повний текст джерелаShen, Chih-Chin, and 沈志欽. "Privacy-Preserving Clustering of Data Streams." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98062034798829819799.
Повний текст джерела東吳大學
資訊管理學系
97
Due to most historic studies on privacy-preserving data mining placed importance on the security of the massive amount of data from static database, consequently data undergoing privacy-preserving often lead to the decline in accuracy of mining result. Furthermore, following by the rapid advancement on internet and telecommunication technology, subsequently data types have transformed from traditional static data into data streams with consecutive, rapid, temporal, and caducous, as well as unpredictable properties. Due to the raising of such data types, traditional privacy-preserving data mining algorithm requiring complex calculation is no longer applicable. As a result, this paper has proposed a method of Privacy-Preserving Clustering of Data Streams (PPCDS) to improve data stream mining procedures while concurrently preserve privacy and a good mining accuracy. PPCDS is mainly composed of two phases: Rotation-Based Perturbation and cluster mining. In the phase of data rotating perturbation, a rotation transformation matrix is applied to rapidly perturb the data streams in order to preserve data privacy. In the cluster mining phase, perturbed data will first establish micro-cluster through optimization of cluster centers, then applying statistical calculation to update micro-cluster, as well as using geometric time frame to allocate and store micro-cluster, and finally input mining result through macro-cluster generation. Two simple data structure is added in the macro-cluster generation process to avoid recalculating the distance between the macro-point and the cluster center in the generation process. This process reduces the repeated calculation time in order to enhance mining efficiency without losing mining accuracy.
Hsieh, Ping-Yen, and 謝秉諺. "A Study of Some Privacy-Preserving Clustering Schemes." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/45450894976786879943.
Повний текст джерела國立臺灣大學
資訊網路與多媒體研究所
96
Clustering is one of the most useful techniques to do some data analysis. But the conventional way to perform clustering usually offends one’s privacy. In the era of digital information, privacy is a very important concern in our daily life. To preserve one’s privacy, we exploit several privacy-preserving clustering schemes in this thesis. In the beginning, introduction to clustering, distributed clustering, and privacy-preserving clustering schemes will be given in order. And then, two major schemes, privacy-preserving k-means clustering and privacy-preserving hierarchical clustering, are illustrated in details. We implement these algorithms and perform experiments with both simulated ones and realistic data sets to learn the characteristics of each privacy-preserving clustering algorithm and evaluate the feasibility and usefulness of privacy-preserving clustering schemes.
Hsieh, Ping-Yen. "A Study of Some Privacy-Preserving Clustering Schemes." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0307200801072600.
Повний текст джерелаCunha, Mariana da Cruz. "Privacy-Preserving Mechanisms for Location Traces." Master's thesis, 2019. http://hdl.handle.net/10316/87304.
Повний текст джерелаLocation-Based Services are increasingly present in our daily lives. However, regardless of the benefits that these services offer to users, the shared data are not always and only used for the initial purpose. These data can be made public or sold, for example, for commercial purposes. The fact that location data contain information that can reveal the person’s identity, routines and habits, raises serious privacy concerns. In order to respond to this problem, there are privacy-preserving mechanisms, namely, for obfuscation and for anonymization of data. However, the correlation between location reports, which can potentially be used by an adversary to estimate the position of the user, has been underlooked in privacy protection. The aim of this thesis is to develop a user-centric Location Privacy-Preserving Mechanism, that is, a mechanism that protects privacy of a user at collection time. In addition, it is intended to protect the users not only against single reports, but also over time, against continuous reports. In this latter scenario, we intent to develop a protection mechanism that is suitable to different frequency of updates and/or to the correlation between reports as to mitigate possible privacy violations that advent from exploring these intrinsic characteristics of location data. Towards this end, we started by evaluating the impact of the frequency of updates on location privacy. For that, we implemented a state-of-the-art tracking attack that allows us to assess the effect of the frequency of updates by estimating the exact user locations. According to the performed analysis, we developed a new mechanism based on geo-indistinguishability that creates obfuscation clusters to aggregate closer locations. This developed mechanism is designated clustering geo-indistinguishability. To evaluate the utility of the mechanism, we resorted to a real use-case based on geofencing. Lastly, the evaluation of the mechanism enables us to conclude that it safeguards the level of privacy and the utility of continuous reports of location data, in a way that it can still be used for the purpose of a service.
Os serviços baseados em localização estão cada vez mais presentes no nosso quotidiano. No entanto, apesar do benefício que estes serviços oferecem aos utilizadores, os dados partilhados nem sempre são usados apenas com o propósito inicial. Estes dados podem ser tornados públicos ou vendidos, por exemplo, para fins comerciais. O facto dos dados de localização conterem informações passíveis de revelar a identidade, as rotinas e os hábitos de uma pessoa, levantam sérias preocupações de privacidade. Para dar resposta a este desiderato, existem mecanismos de preservação de privacidade, nomeadamente, de ofuscação e anonimização dos dados. Contudo, a correlação entre os dados de localização partilhados, que pode ser usada por um adversário para estimar a posição de um utilizador, tem sido negligenciada na proteção da privacidade. O objetivo desta tese é desenvolver um mecanismo de preservação de privacidade de localização centrado no utilizador, isto é, um mecanismo que proteja os utilizadores no momento da partilha de dados. Para além disso, pretende-se proteger o utilizador não só quando este reporta localizações únicas, mas também ao longo do tempo, isto é, quando reporta localizações de modo contínuo. Neste último cenário, pretendemos desenvolver um mecanismo de proteção que seja adequado a diferentes frequências de atualização de localização e/ou à correlação existente entre as localizações partilhadas, de modo a mitigar possíveis violações de privacidade que advenham da exploração destas características intrínsecas dos dados de localização. Neste sentido, começámos por avaliar o impacto da frequência na privacidade de localização. Para tal, implementámos um ataque considerado estado da arte que permite localizar o utilizador ao longo do tempo e do espaço, viabilizando a avaliação do efeito da frequência através da estimação da localização exata do utilizador. De acordo com a análise efetuada, desenvolvemos um mecanismo novo baseado em geo-indistinguishability que cria áreas de ofuscação para agregar localizações próximas. O mecanismo desenvolvido é designado clustering geo-indistinguisahbility. Para avaliar a utilidade do mecanismo, utilizámos um caso de uso real baseado em geofencing. Por fim, a avaliação do mecanismo permitiu-nos concluir que este salvaguarda o nível de privacidade e a utilidade dos dados, de tal modo que continuam a poder ser usados para o propósito do serviço.
Outro - Este trabalho é suportado pelos projetos SWING2 (PTDC/EEI-TEL/3684/2014) e Mobiwise (P2020 SAICTPAC/001/2015), financiado pelos Fundos Europeus Estruturais e de Investimento (FEEI) Europeus através do Programa Operacional Competitividade e Internacionalização - COMPETE 2020 e por Fundos Nacionais através da FCT - Fundação para a Ciência e a Tecnologia no âmbito do projeto POCI-01-0145-FEDER-016753, e pelo Fundo Europeu de Desenvolvimento Regional.
Частини книг з теми "Privacy preserving clustering"
Jha, Somesh, Luis Kruger, and Patrick McDaniel. "Privacy Preserving Clustering." In Computer Security – ESORICS 2005, 397–417. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11555827_23.
Повний текст джерелаUpmanyu, Maneesh, Anoop M. Namboodiri, Kannan Srinathan, and C. V. Jawahar. "Efficient Privacy Preserving K-Means Clustering." In Intelligence and Security Informatics, 154–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13601-6_17.
Повний текст джерелаKumar, K. Anil, and C. Pandu Rangan. "Privacy Preserving DBSCAN Algorithm for Clustering." In Advanced Data Mining and Applications, 57–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_7.
Повний текст джерелаHamidi, Mona, Mina Sheikhalishahi, and Fabio Martinelli. "Privacy Preserving Expectation Maximization (EM) Clustering Construction." In Distributed Computing and Artificial Intelligence, 15th International Conference, 255–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94649-8_31.
Повний текст джерелаSheikhalishahi, Mina, Mona Hamidi, and Fabio Martinelli. "Privacy Preserving Collaborative Agglomerative Hierarchical Clustering Construction." In Communications in Computer and Information Science, 261–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25109-3_14.
Повний текст джерелаLiang, Shaopeng, Haomiao Yang, Qixian Zhou, and Minglu Zhang. "Privacy-Preserving and Outsourced Density Peaks Clustering Algorithm." In Communications in Computer and Information Science, 538–52. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9739-8_40.
Повний текст джерелаHu, Xiaoyi, Liping Lu, Dongdong Zhao, Jianwen Xiang, Xing Liu, Haiying Zhou, Shengwu Xiong, and Jing Tian. "Privacy-Preserving K-Means Clustering Upon Negative Databases." In Neural Information Processing, 191–204. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04212-7_17.
Повний текст джерелаLi, Wenye. "A Unified Framework for Privacy Preserving Data Clustering." In Neural Information Processing, 319–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_40.
Повний текст джерелаLi, Wenye. "Privacy Preserving Clustering: A k-Means Type Extension." In Neural Information Processing, 319–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_39.
Повний текст джерелаLuo, Junwei, Xun Yi, Fengling Han, Xuechao Yang, and Xu Yang. "An Efficient Clustering-Based Privacy-Preserving Recommender System." In Network and System Security, 387–405. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23020-2_22.
Повний текст джерелаТези доповідей конференцій з теми "Privacy preserving clustering"
Bozdemir, Beyza, Sébastien Canard, Orhan Ermis, Helen Möllering, Melek Önen, and Thomas Schneider. "Privacy-preserving Density-based Clustering." In ASIA CCS '21: ACM Asia Conference on Computer and Communications Security. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3433210.3453104.
Повний текст джерелаDash, Sanjit Kumar, Debi Pr Mishra, Ranjita Mishra, and Sweta Dash. "Privacy preserving K-Medoids clustering." In the Second International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393216.2393290.
Повний текст джерелаDas, Ananda Swarup, and Kannan Srinathan. "Privacy Preserving Cooperative Clustering Service." In 15th International Conference on Advanced Computing and Communications (ADCOM 2007). IEEE, 2007. http://dx.doi.org/10.1109/adcom.2007.52.
Повний текст джерелаZhan, Justin. "Privacy preserving K-Medoids clustering." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4414177.
Повний текст джерелаLiu, Jinfei, Joshua Zhexue Huang, Jun Luo, and Li Xiong. "Privacy preserving distributed DBSCAN clustering." In the 2012 Joint EDBT/ICDT Workshops. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2320765.2320819.
Повний текст джерелаLuong, The Dung, and Tu Bao Ho. "Privacy Preserving EM-Based Clustering." In 2009 IEEE-RIVF International Conference on Computing and Communication Technologies. IEEE, 2009. http://dx.doi.org/10.1109/rivf.2009.5174654.
Повний текст джерелаFan-rong Meng, Bin Liu, and Chu-jiao Wang. "Privacy preserving clustering over distributed data." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579456.
Повний текст джерелаBiswas, Chandan, Debasis Ganguly, Dwaipayan Roy, and Ujjwal Bhattacharya. "Privacy Preserving Approximate K-means Clustering." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357384.3357969.
Повний текст джерелаVashkevich, Alexey V., and Vagim G. Zhukov. "Privacy-preserving clustering using C-means." In 2015 International Siberian Conference on Control and Communications (SIBCON). IEEE, 2015. http://dx.doi.org/10.1109/sibcon.2015.7147017.
Повний текст джерелаKalita, M., D. K. Bhattacharyya, and M. Dutta. "Privacy Preserving Clustering-A Hybrid Approach." In 2008 16th International Conference on Advanced Computing and Communications (ADCOM). IEEE, 2008. http://dx.doi.org/10.1109/adcom.2008.4760438.
Повний текст джерела