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Dissertations / Theses on the topic 'Privacy Preserving Data Mining (PPDM)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Swapna, B., and R. VijayaPrakash. "Privacy Preserving Data Mining Operations without Disrupting Data Quality." International Journal of Computer Science and Network (IJCSN), 2012. http://hdl.handle.net/10150/271473.

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Data mining operations have become prevalent as they can extract trends or patterns that help in taking good business decisions. Often they operate on large historical databases or data warehouses to obtain actionable knowledge or business intelligence that helps in taking well informed decisions. In the data mining domain there came many tools to perform data mining operations. These tools are best used to obtain actionable knowledge from data. Manually doing this is not possible as the data is very huge and takes lot of time. Thus the data mining domain is being improved in a rapid

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2

Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

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Lin, Zhenmin. "Privacy Preserving Distributed Data Mining." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/9.

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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 basi

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4

Balla, Stefano. "Privacy-Preserving Data Mining: un approccio verticale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17517/.

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La crescente disponibilità dei dati, oltre a portare grandi benefici, ha portato alla luce diversi rischi dipendenti dell'esposizione di informazioni confidenziali. Inoltre il metodo di estrazione di informazioni detto Data Mining ha posto un ulteriore problema dando la possibilità di estrarre informazioni sensibili. Questa tesi tratta il tema della privacy sotto tre livelli principali: Privacy-Preserving Data Providing, Privacy-Preserving Data Collecting e Privacy-Preserving Data Mining. Questi livelli rappresentano la divisione del ciclo dei dati in tre tappe in cui i dati prima vengono fo

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5

Ma, Jianjie. "Learning from perturbed data for privacy-preserving data mining." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Dissertations/Summer2006/j%5Fma%5F080406.pdf.

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6

HajYasien, Ahmed. "Preserving Privacy in Association Rule Mining." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365286.

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With the development and penetration of data mining within different fields and disciplines, security and privacy concerns have emerged. Data mining technology which reveals patterns in large databases could compromise the information that an individual or an organization regards as private. The aim of privacy-preserving data mining is to find the right balance between maximizing analysis results (that are useful for the common good) and keeping the inferences that disclose private information about organizations or individuals at a minimum. In this thesis we present a new classification for p

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7

Casas, Roma Jordi. "Privacy-preserving and data utility in graph mining." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/285566.

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En los últimos años, ha sido puesto a disposición del público una gran cantidad de los datos con formato de grafo. Incrustado en estos datos hay información privada acerca de los usuarios que aparecen en ella. Por lo tanto, los propietarios de datos deben respetar la privacidad de los usuarios antes de liberar los conjuntos de datos a terceros. En este escenario, los procesos de anonimización se convierten en un proceso muy importante. Sin embargo, los procesos de anonimización introducen, generalmente, algún tipo de ruido en los datos anónimos y también en sus resultados en procesos de mine

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Nabar, Shubha Umesh. "Models and algorithms for privacy-preserving data mining /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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9

Secretan, James. "AN ARCHITECTURE FOR HIGH-PERFORMANCE PRIVACY-PRESERVING AND DISTRIBUTED DATA MINING." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2504.

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This dissertation discusses the development of an architecture and associated techniques to support Privacy Preserving and Distributed Data Mining. The field of Distributed Data Mining (DDM) attempts to solve the challenges inherent in coordinating data mining tasks with databases that are geographically distributed, through the application of parallel algorithms and grid computing concepts. The closely related field of Privacy Preserving Data Mining (PPDM) adds the dimension of privacy to the problem, trying to find ways that organizations can collaborate to mine their databases collectively,

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10

Liu, Lian. "PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.

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Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbati

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Sehatkar, Morvarid. "Towards a Privacy Preserving Framework for Publishing Longitudinal Data." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31629.

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Recent advances in information technology have enabled public organizations and corporations to collect and store huge amounts of individuals' data in data repositories. Such data are powerful sources of information about an individual's life such as interests, activities, and finances. Corporations can employ data mining and knowledge discovery techniques to extract useful knowledge and interesting patterns from large repositories of individuals' data. The extracted knowledge can be exploited to improve strategic decision making, enhance business performance, and improve services. However, pe

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12

Parameswaran, Rupa. "A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11459.

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Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters and #146; lists, pose a threat to personal privacy. The concern over individual privacy has led to the development of legal codes for safeguarding privacy in several countries. However, the ignorance of individuals as well as loopholes in the systems, have led to information breaches even in the presence of such rules and regulations. Protection against data privacy requires modification of the data itself. The

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Alotaibi, Khaled. "Non-metric multi-dimensional scaling for distance-based privacy-preserving data mining." Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/52228/.

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Recent advances in the field of data mining have led to major concerns about privacy. Sharing data with external parties for analysis puts private information at risk. The original data are often perturbed before external release to protect private information. However, data perturbation can decrease the utility of the output. A good perturbation technique requires balance between privacy and utility. This study proposes a new method for data perturbation in the context of distance-based data mining. We propose the use of non-metric multi-dimensional scaling (MDS) as a suitable technique to pe

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14

KINSEY, MICHAEL LOY. "PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448.

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15

Li, Zhizhou. "Multi-Scheme Fully Homomorphic Encryptions And Its Application In Privacy Preserving Data Mining." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1430760068.

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16

Jafer, Yasser. "Task Oriented Privacy-preserving (TOP) Technologies Using Automatic Feature Selection." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34320.

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A large amount of digital information collected and stored in datasets creates vast opportunities for knowledge discovery and data mining. These datasets, however, may contain sensitive information about individuals and, therefore, it is imperative to ensure that their privacy is protected. Most research in the area of privacy preserving data publishing does not make any assumptions about an intended analysis task applied on the dataset. In many domains such as healthcare, finance, etc; however, it is possible to identify the analysis task beforehand. Incorporating such knowledge of the ult

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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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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

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18

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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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

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Shepard, Samuel Steven. "Anonymous Opt-Out and Secure Computation in Data Mining." Bowling Green State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1194282001.

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LaMacchia, Carolyn. "Improving the Scalability of an Exact Approach for Frequent Item Set Hiding." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/205.

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Technological advances have led to the generation of large databases of organizational data recognized as an information-rich, strategic asset for internal analysis and sharing with trading partners. Data mining techniques can discover patterns in large databases including relationships considered strategically relevant to the owner of the data. The frequent item set hiding problem is an area of active research to study approaches for hiding the sensitive knowledge patterns before disclosing the data outside the organization. Several methods address hiding sensitive item sets including an exac

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21

Fong, Pui Kuen. "Privacy Preserving Data Mining using Unrealized Data Sets: Scope Expansion and Data Compression." Thesis, 2013. http://hdl.handle.net/1828/4623.

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In previous research, the author developed a novel PPDM method – Data Unrealization – that preserves both data privacy and utility of discrete-value training samples. That method transforms original samples into unrealized ones and guarantees 100% accurate decision tree mining results. This dissertation extends their research scope and achieves the following accomplishments: (1) it expands the application of Data Unrealization on other data mining algorithms, (2) it introduces data compression methods that reduce storage requirements for unrealized training samples and increase data mining per

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22

Sharma, Bikash, and Aman Jain. "Privacy preserving data mining." Thesis, 2007. http://ethesis.nitrkl.ac.in/4218/1/PRIVACY_PRESERVING_DATA_MINING_27.pdf.

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A fruitful direction for future data mining research will be the development of technique that incorporates privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We analyze the possibility of privacy in data mining techniques in two phasesrandomization and reconstruction. Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users t

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23

"Privacy preserving data publishing." Thesis, 2008. http://library.cuhk.edu.hk/record=b6074672.

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The advance of information technologies has enabled various organizations (e.g., census agencies, hospitals) to collect large volumes of sensitive personal data (e.g., census data, medical records). Due to the great research value of such data, it is often released for public benefit purposes, which, however, poses a risk to individual privacy. A typical solution to this problem is to anonymize the data before releasing it to the public. In particular, the anonymization should be conducted in a careful manner, such that the published data not only prevents an adversary from inferring sensitive

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24

Brickell, Justin Lee. "Privacy-preserving computation for data mining." 2009. http://hdl.handle.net/2152/7538.

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As data mining matures as a field and develops more powerful algorithms for discovering and exploiting patterns in data, the amount of data about individuals that is collected and stored continues to rapidly increase. This increase in data heightens concerns that data mining violates individual privacy. The goal of data mining is to derive aggregate conclusions, which should not reveal sensitive information. However, the data-mining algorithms run on databases containing information about individuals which may be sensitive. The goal of privacy-preserving data mining is to provide high-quality

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Sinha, B. K., and J. Kumar. "Privacy Preserving Clustering In Data Mining." Thesis, 2010. http://ethesis.nitrkl.ac.in/1789/1/thesis.pdf.

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Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Such data include shopping habits, criminal records, medical history, credit records etc .On one hand such data is an important asset to business organization and governments for decision making by analyzing it .On the other hand privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy data owner must come up with a solution which achieves the dual

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Illia, Leshchenko, and Leshchenko Illia. "Data Security: Modified Privacy-Preserving Data Mining Algorithm." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/60128231051349181874.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>104<br>Nowadays the majority of people in developed countries are using the Internet. Therefore, all of them give their personal data to third-parties, which can use it on specified conditions. However, none of the Internet websites are completely protected from malicious users, especially when those third-parties are using data mining technique, which is pretty common now. This thesis focuses on inventing a modified algorithm to provide better personal data security comparing to existing ones. This algorithm reduces a leakage of personal information for public use.

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Babu, Korra Sathya. "Utility-Based Privacy Preserving Data Publishing." Thesis, 2013. http://ethesis.nitrkl.ac.in/5487/1/Korra_Sathya_Babu.pdf.

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Advances in data collection techniques and need for automation triggered in proliferation of a huge amount of data. This exponential increase in the collection of personal information has for some time represented a serious threat to privacy. With the advancement of technologies for data storage, data mining, machine learning, social networking and cloud computing, the problem is further fueled. Privacy is a fundamental right of every human being and needs to be preserved. As a counterbalance to the socio-technical transformations, most nations have both general policies on preserving priva

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Williams, James. "Unrealization approaches for privacy preserving data mining." Thesis, 2010. http://hdl.handle.net/1828/3156.

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This thesis contains a critical evaluation of the unrealization approach to privacy preserving data mining. We cover a fair bit of ground, making numerous contributions to the existing literature. First, we present a comprehensive and accurate analysis of the challenges posed by data mining to privacy. Second, we put the unrealization approach on firmer ground by providing proofs of previously unproven claims, using the multi-relational algebra. Third, we extend the unrealization approach to the C4.5 algorithm. Fourth, we evaluate the algorithm's space requirements on three representative data

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Wu, Wen-chung, and 吳文群. "Privacy-Preserving Frequent-Itemset Mining of Data Streams." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/8s4by4.

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碩士<br>東吳大學<br>資訊科學系<br>96<br>Compared to traditional static databases, data streams have the following characteristics: (1) Data flows in with fast speed; (2) The amount of data is enormous; (3) Data distribution changes constantly with time; (4) Immediate response is required. Due to the emergence of this new type of data, data stream mining has recently become a very popular research issue. There have been many studies proposing efficient mining algorithms for data streams. However, to the best of out knowledge, there is no research that studies the privacy preservation issue of data stream

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Yang, Kuo-Tung, and 楊國棟. "Several Heuristic Approaches to Privacy-Preserving Data Mining." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/51265621784508458978.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>98<br>Data mining technology can help extract useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Some sensitive or private information about individuals, businesses and organizations needs to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important issue in recent years. In this thesis, we propose three approaches for modifying original databases in order to hide sensitive itemsets. The first one is cal

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31

Xu, Zhuojia. "Analysis of privacy preserving distributed data mining protocols." Thesis, 2011. https://vuir.vu.edu.au/16047/.

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This thesis studies the features and performance of privacy-preserving distributed data mining protocols published as journal articles and conference proceedings from 1999 to 2009. It examines the topics and settings of various privacy-preserving distributed data mining protocols. This thesis aims to provide a framework for classifying privacy-preserving distributed data mining protocols and compare the performance of different protocols based on the outcome of the classification scheme.

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Hou, Pei-Wen, and 侯佩文. "A Study of Reversible Privacy-preserving Data Mining Technology." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/48a2yt.

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碩士<br>臺中技術學院<br>資訊工程系碩士班<br>99<br>Privacy-preserving data mining (PPDM) may be used by perturbing or deleting information to protect the privacy data while preserving the analyzed knowledge to be similar to its original database. This method can effectively prevent interested parties from speculating the possibility of protected data by inference. However, the irreversible characteristic of PPDM resulted in ineffective verification of the knowledge in the database when critical decision fields were protected. In this thesis, the PPDM characteristic of protecting privacy data is adopted with th

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Liu, Li. "Perturbation based privacy preserving data mining techniques for real-world data /." 2008. http://proquest.umi.com/pqdweb?did=1546799921&sid=7&Fmt=2&clientId=10361&RQT=309&VName=PQD.

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Lin, Hsiu-Chung, and 林秀忠. "An Entropy-Based Hiding Algorithm for Privacy Preserving Data Mining." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/6887a4.

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Wu, Chieh-Ming, and 吳界明. "Data mining for generalized association rules and privacy preservingData mining for generalized association rules and privacy preserving." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/85543535661382633122.

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博士<br>國立雲林科技大學<br>工程科技研究所博士班<br>99<br>Data mining is an analysis method used to extract the unknown and latent information that hides in large dataset which has usable information. In the last few years the data mining model and method have long-term progress and the association rule mining is most often applied. The association rule research focus on discussion how to discover single level association rule effectiveness in the large dataset. In the recent years more and more researchers start to study the problem of multiple level association rules that was advantageous in the knowledge econo

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"Privacy preserving in serial data and social network publishing." 2010. http://library.cuhk.edu.hk/record=b5894365.

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Liu, Jia.<br>"August 2010."<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.<br>Includes bibliographical references (p. 69-72).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Related Work --- p.3<br>Chapter 3 --- Privacy Preserving Network Publication against Structural Attacks --- p.5<br>Chapter 3.1 --- Background and Motivation --- p.5<br>Chapter 3.1.1 --- Adversary knowledge --- p.6<br>Chapter 3.1.2 --- Targets of Protection --- p.7<br>Chapter 3.1.3 --- Challenges and Contributions --- p.10<br>Chapter 3.2 --- Preliminaries and P

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Yuan-Chung, Chang, and 張淵琮. "The study on privacy-preserving association rule mining for data sharing." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/10395401470955928759.

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碩士<br>國防大學中正理工學院<br>資訊科學研究所<br>96<br>Data mining is a popular technology for the application of database in recent years. In addition, association rule mining has been applied to discover the interesting relationships hidden in large databases by the administrators of some organizations. The discovered information is valuable for making decisions. Furthermore, international information organizations have dedicated to establish an “Information Sharing and Analysis Center (ISAC)” for sharing the information and providing early warning of critical events. Similarly, through data sharing from diff

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Chang, Yin-Ming, and 張穎銘. "Protocol design for privacy-preserving data mining using partial homomorphic encryption." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/14961139955051252325.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>101<br>With the advance of computing power, data mining techniques can extract useful information from large amount of data. In 2012, 2.5 quintillion bytes of data (1 follow 18 zeroes) are created every day. Data privacy is of utmost concern for distributed data mining across multiple parties, which may be competitors. In this thesis, we focus on the privacy preserving techniques in distributed data mining algorithms. We propose two protocols|multi-party association rule mining (MP-ARM) and multi-party decision tree learning (MP-DTL). Both protocols use partial ho

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Chen, Ling-Chieh, and 陳凌潔. "A study of Privacy Preserving Data Mining Based on Hash Function." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/73184017853463753993.

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碩士<br>中國文化大學<br>資訊安全產業研發碩士專班<br>98<br>Following the dawn of the Information Technology era and the vigorous development of the Internet, data mining has become a technology that is used in various fields to gather knowledge or as a basis for decision-making, and even sometimes as an indicator for profitable marketing. This being said, while devoted in the improvement and innovation of data mining technology, private or confidential data could be discovered by anyone. In order to resolve the two large security issues, the information security and the knowledge security of data mining, research

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Pinto, Gustavo de Castro Nogueira. "A Secure and Privacy Preserving Approach to Medical Data Mining Applications." Master's thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/114065.

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Dlamini, Mbuso Gerald, and Mbuso Gerald Dlamini. "Privacy Preserving Data Mining and Association Rule Sharing for Business Intelligence." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/7sefy2.

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碩士<br>國立臺北科技大學<br>電資學院外國學生專班<br>103<br>The growing interest in business intelligence, cloud computing and technological advancements has necessitated the usage of data mining as a service in order to ensure business success and sustainability. As a result data mining techniques are becoming more useful to discover and understand unknown customer patterns and their behaviors through association rules discovered from the transactional databases. The lack of expertise and computational resources have compelled companies to outsource data mining activities as it is a complex and expensive exercise

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Pinto, Gustavo de Castro Nogueira. "A Secure and Privacy Preserving Approach to Medical Data Mining Applications." Dissertação, 2018. https://repositorio-aberto.up.pt/handle/10216/114065.

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chunlakan, Khunanon, and 朱恩榮. "A Study of K-anonymity Model for Privacy Preserving Data Mining." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2r9pqh.

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碩士<br>國立虎尾科技大學<br>資訊工程系碩士班<br>106<br>In the information technologies era, there are have a lot of industries that collected the data from the customer. And later those data are released for research or analysis purposes. The public data contain the sensitive information like salary or disease. K-anonymity is one of the most widely use privacy preservation model. Because of its famous method, there are a lot of algorithms that have been introduced. In this thesis, we will compare between the most widely used three algorithms of k-anonymity. By performing a comparison of three algorithms to meas

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Paulet, Russell. "Design and analysis of privacy-preserving protocols." Thesis, 2013. https://vuir.vu.edu.au/24832/.

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More and more of our daily activities are using the Internet to provide an easy way to get access to instant information. The equipment enabling these interactions is also storing information such as: access time, where you are, and what you plan to do. The ability to store this information is very convenient but is also the source of a major concern: once data are stored, it must be protected. If the data was left unprotected, then people would feel reluctant to use the service. The aim of this thesis is to remove the need to store such data, while still maintaining overall utility, b

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Hlatshwayo, Muzi Wandile, and Muzi Wandile Hlatshwayo. "Privacy Preserving Data Mining and Association Rule Sharing Using The High Lift Algorithm." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/6vyhq3.

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碩士<br>國立臺北科技大學<br>電機工程研究所<br>105<br>The growing need in Companies to make it in business and to stay relevant to customer needs and reactions, has resulted in many companies turning to Business Intelligence. This is done through outsourcing, however the idea of maintaining the company’s secrets throughout the process then becomes an issue of utmost importance thus bringing forth the need for Privacy Preserving Data Mining (PPDM). Little research has been done in this field and especially in areas involving the market basket analysis. Current researches have focused on developing privacy preser

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Park, Yubin. "CUDIA : a probabilistic cross-level imputation framework using individual auxiliary information." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4746.

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In healthcare-related studies, individual patient or hospital data are not often publicly available due to privacy restrictions, legal issues or reporting norms. However, such measures may be provided at a higher or more aggregated level, such as state-level, county-level summaries or averages over health zones such as Hospital Referral Regions (HRR) or Hospital Service Areas (HSA). Such levels constitute partitions over the underlying individual level data, which may not match the groupings that would have been obtained if one clustered the data based on individual-level attributes. Moreover,

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Al-Ahmadi, Mohammad Saad. "Adapting masking techniques for estimation problems involving non-monotonic relationships in privacy-preserving data mining." 2006. http://digital.library.okstate.edu/etd/umi-okstate-1982.pdf.

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Wang, Peter Shaojui, and 王紹睿. "Design of a Privacy-Preserving Data Mining System Based on Differential Privacy Using Additive-Homomorphic Proxy Re-Encryption Protocol Against Insider Attacks." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/63639699902141668295.

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博士<br>國立臺灣大學<br>資訊工程學研究所<br>104<br>In this thesis, we consider a new insider threat for the privacy preserving work of distributed kernel-based data mining (DKBDM), such as distributed Support Vector Machine (SVM). Among several known data breaching problems, those associated with insider attacks have been rising significantly, making this one of the fastest growing types of security breaches. Once considered a negligible concern, insider attacks have risen to be one of the top three central data violations. Insider-related research involving the distribution of kernel-based data mining is lim

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Καγκλής, Βασίλειος. "Novel frequent itemset hiding techniques and their evaluation." Thesis, 2015. http://hdl.handle.net/10889/8746.

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Advances in data collection and data storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous volumes of data to be stored efficiently. Most of the times, these vast amounts of data cannot be used as they are. A data processing should first take place, so as to extract the useful knowledge. After the useful knowledge is mined, it can be used in several ways depending on the nature of the data. Quite often, companies and organizations are willing to share data for the sake of mutual benefit. However, these

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