Готові списки джерел за темами / Privacy preserving clustering

Добірка наукової літератури з теми "Privacy preserving clustering"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Privacy preserving clustering"

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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.

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Анотація:
Abstract Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today’s four most efficient fully private clustering protocols by Cheon et al. (SAC’19), Meng et al. (ArXiv’19), Mohassel et al. (PETS’20), and Bozdemir et al. (ASIACCS’21) with respect to communication, computation, and clustering quality. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.
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Lyu, 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.

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3

Gao, 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.

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This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.
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Mohassel, 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.

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Анотація:
AbstractClustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacypreserving context.Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the amortizing setting, when one needs to compute the distance from the same point to other points. Furthermore, we construct a customized garbled circuit for computing the minimum value among shared values.We believe these new constructions may be of independent interest. We implement and evaluate our protocols to demonstrate their practicality and show that they are able to train datasets that are much larger and faster than in the previous work. The numerical results also show that the proposed protocol achieve almost the same accuracy compared to a K-means plain-text clustering algorithm.
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Ni, 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.

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Jahan, 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.

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Wei, 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.

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Анотація:
Privacy-preserving machine learning has become an important study at present due to privacy policies. However, the efficiency gap between the plain-text algorithm and its privacy-preserving version still exists. In this paper, we focus on designing a novel secret-sharing-based K-means clustering algorithm. Particularly, we present an efficient privacy-preserving K-means clustering algorithm based on replicated secret sharing with honest-majority in the semi-honest model. More concretely, the clustering task is outsourced to three semi-honest computing servers. Theoretically, the proposed privacy-preserving scheme can be proven with full data privacy. Furthermore, the experimental results demonstrate that our proposed privacy version reaches the same accuracy as the plain-text one. Compared to the existing privacy-preserving scheme, our proposed protocol can achieve about 16.5×–25.2× faster computation and 63.8×–68.0× lower communication. Consequently, the proposed privacy-preserving scheme is suitable for secret-sharing-based secure outsourced computation.
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Oliveira, 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.

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Nguyen, 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.

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İnan, 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.

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Більше джерел

Дисертації з теми "Privacy preserving clustering"

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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.

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Cui, Yingjie. "A study on privacy-preserving clustering." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4357225X.

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Thapa, Nirmal. "CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/15.

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Анотація:
Data are valuable assets to any organizations or individuals. Data are sources of useful information which is a big part of decision making. All sectors have potential to benefit from having information. Commerce, health, and research are some of the fields that have benefited from data. On the other hand, the availability of the data makes it easy for anyone to exploit the data, which in many cases are private confidential data. It is necessary to preserve the confidentiality of the data. We study two categories of privacy: Data Value Hiding and Data Pattern Hiding. Privacy is a huge concern but equally important is the concern of data utility. Data should avoid privacy breach yet be usable. Although these two objectives are contradictory and achieving both at the same time is challenging, having knowledge of the purpose and the manner in which it will be utilized helps. In this research, we focus on some particular situations for clustering and classification problems and strive to balance the utility and privacy of the data. In the first part of this dissertation, we propose Nonnegative Matrix Factorization (NMF) based techniques that accommodate constraints defined explicitly into the update rules. These constraints determine how the factorization takes place leading to the favorable results. These methods are designed to make alterations on the matrices such that user-specified cluster properties are introduced. These methods can be used to preserve data value as well as data pattern. As NMF and K-means are proven to be equivalent, NMF is an ideal choice for pattern hiding for clustering problems. In addition to the NMF based methods, we propose methods that take into account the data structures and the attribute properties for the classification problems. We separate the work into two different parts: linear classifiers and nonlinear classifiers. We propose two different solutions based on the classifiers. We study the effect of distortion on the utility of data. We propose three distortion measurement metrics which demonstrate better characteristics than the traditional metrics. The effectiveness of the measures is examined on different benchmark datasets. The result shows that the methods have the desirable properties such as invariance to translation, rotation, and scaling.
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Lin, Zhenmin. "Privacy Preserving Distributed Data Mining." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/9.

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Анотація:
Privacy preserving distributed data mining aims to design secure protocols which allow multiple parties to conduct collaborative data mining while protecting the data privacy. My research focuses on the design and implementation of privacy preserving two-party protocols based on homomorphic encryption. I present new results in this area, including new secure protocols for basic operations and two fundamental privacy preserving data mining protocols. I propose a number of secure protocols for basic operations in the additive secret-sharing scheme based on homomorphic encryption. I derive a basic relationship between a secret number and its shares, with which we develop efficient secure comparison and secure division with public divisor protocols. I also design a secure inverse square root protocol based on Newton's iterative method and hence propose a solution for the secure square root problem. In addition, we propose a secure exponential protocol based on Taylor series expansions. All these protocols are implemented using secure multiplication and can be used to develop privacy preserving distributed data mining protocols. In particular, I develop efficient privacy preserving protocols for two fundamental data mining tasks: multiple linear regression and EM clustering. Both protocols work for arbitrarily partitioned datasets. The two-party privacy preserving linear regression protocol is provably secure in the semi-honest model, and the EM clustering protocol discloses only the number of iterations. I provide a proof-of-concept implementation of these protocols in C++, based on the Paillier cryptosystem.
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Chatalic, Antoine. "Efficient and privacy-preserving compressive learning." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S030.

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Анотація:
Ce travail de thèse, qui se situe à l'interface entre traitement du signal, informatique et statistiques, vise à l'élaboration de méthodes d'apprentissage automatique à grande échelle et de garanties théoriques associées. Il s'intéresse en particulier à l'apprentissage compressif, un paradigme dans lequel le jeu de données est compressé en un unique vecteur de moments généralisés aléatoires, appelé le sketch et contenant l'information nécessaire pour résoudre de manière approchée la tâche d'apprentissage considérée. Le schéma de compression utilisé permet de tirer profit d'une architecture distribuée ou de traiter des données en flux, et a déjà été utilisé avec succès sur plusieurs tâches d'apprentissage non-supervisé : partitionnement type k-moyennes, modélisation de densité avec modèle de mélange gaussien, analyse en composantes principales. Les contributions de la thèse s'intègrent dans ce cadre de plusieurs manières. D'une part, il est montré qu'en bruitant le sketch, des garanties de confidentialité (différentielle) peuvent être obtenues; des bornes exactes sur le niveau de bruit requis sont données, et une comparaison expérimentale permet d'établir que l'approche proposée est compétitive vis-à-vis d'autres méthodes récentes. Ensuite, le schéma de compression est adapté pour utiliser des matrices aléatoires structurées, qui permettent de réduire significativement les coûts de calcul et rendent possible l'utilisation de méthodes compressives sur des données de grande dimension. Enfin, un nouvel algorithme basé sur la propagation de convictions est proposé pour résoudre la phase d'apprentissage (à partir du sketch) pour le problème de partitionnement type k-moyennes
The 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
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Chen, Keke. "Geometric Methods for Mining Large and Possibly Private Datasets." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11561.

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Анотація:
With the wide deployment of data intensive Internet applications and continued advances in sensing technology and biotechnology, large multidimensional datasets, possibly containing privacy-conscious information have been emerging. Mining such datasets has become increasingly common in business integration, large-scale scientific data analysis, and national security. The proposed research aims at exploring the geometric properties of the multidimensional datasets utilized in statistical learning and data mining, and providing novel techniques and frameworks for mining very large datasets while protecting the desired data privacy. The first main contribution of this research is the development of iVIBRATE interactive visualization-based approach for clustering very large datasets. The iVIBRATE framework uniquely addresses the challenges in handling irregularly shaped clusters, domain-specific cluster definition, and cluster-labeling of the data on disk. It consists of the VISTA visual cluster rendering subsystem, and the Adaptive ClusterMap Labeling subsystem. The second main contribution is the development of ``Best K Plot'(BKPlot) method for determining the critical clustering structures in multidimensional categorical data. The BKPlot method uniquely addresses two challenges in clustering categorical data: How to determine the number of clusters (the best K) and how to identify the existence of significant clustering structures. The method consists of the basic theory, the sample BKPlot theory for large datasets, and the testing method for identifying no-cluster datasets. The third main contribution of this research is the development of the theory of geometric data perturbation and its application in privacy-preserving data classification involving single party or multiparty collaboration. The key of geometric data perturbation is to find a good randomly generated rotation matrix and an appropriate noise component that provides satisfactory balance between privacy guarantee and data quality, considering possible inference attacks. When geometric perturbation is applied to collaborative multiparty data classification, it is challenging to unify the different geometric perturbations used by different parties. We study three protocols under the data-mining-service oriented framework for unifying the perturbations: 1) the threshold-satisfied voting protocol, 2) the space adaptation protocol, and 3) the space adaptation protocol with a trusted party. The tradeoffs between the privacy guarantee, the model accuracy and the cost are studied for the protocols.
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Shen, Chih-Chin, and 沈志欽. "Privacy-Preserving Clustering of Data Streams." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98062034798829819799.

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Анотація:
碩士
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.
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Hsieh, Ping-Yen, and 謝秉諺. "A Study of Some Privacy-Preserving Clustering Schemes." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/45450894976786879943.

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Анотація:
碩士
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.
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Hsieh, Ping-Yen. "A Study of Some Privacy-Preserving Clustering Schemes." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0307200801072600.

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Cunha, Mariana da Cruz. "Privacy-Preserving Mechanisms for Location Traces." Master's thesis, 2019. http://hdl.handle.net/10316/87304.

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Анотація:
Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia
Location-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.
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Частини книг з теми "Privacy preserving clustering"

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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.

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2

Upmanyu, 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.

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3

Kumar, 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.

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4

Hamidi, 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.

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Sheikhalishahi, 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.

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Liang, 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.

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Hu, 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.

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Li, 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.

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9

Li, 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.

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10

Luo, 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.

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Тези доповідей конференцій з теми "Privacy preserving clustering"

1

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.

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2

Dash, 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.

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3

Das, 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.

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4

Zhan, 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.

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5

Liu, 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.

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6

Luong, 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.

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7

Fan-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.

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8

Biswas, 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.

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9

Vashkevich, 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.

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10

Kalita, 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.

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