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Dissertations / Theses on the topic 'Privacy Preserving Data Publishing (PPDP)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Shang, Hui. "Privacy Preserving Kin Genomic Data Publishing." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1594835227299524.

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2

Lin, Zehua. "Privacy Preserving Social Network Data Publishing." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1610045108271476.

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3

Chen, Xiaoqiang. "Privacy Preserving Data Publishing for Recommender System." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155785.

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Driven by mutual benefits, exchange and publication of data among various parties is an inevitable trend. However, released data often contains sensitive information thus direct publication violates individual privacy. This undertaking is in the scope of privacy preserving data publishing (PPDP). Among many privacy models, K- anonymity framework is popular and well-studied, it protects data by constructing groups of anonymous records such that each record in the table released is covered by no fewer than k-1 other records. This thesis investigates different privacy models and focus on achievin

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4

Wang, Hui. "Secure query answering and privacy-preserving data publishing." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31721.

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The last several decades have witnessed a phenomenal growth in the networking infrastructure connecting computers all over the world. The Web has now become an ubiquitous channel for information sharing and dissemination. More and more data is being exchanged and published on the Web. This growth has created a whole new set of research challenges, while giving a new spin to some existing ones. For example, XML(eXtensible Markup Language), a self-describing and semi-structured data format, has emerged as the standard for representing and exchanging data between applications across the Web. An i

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5

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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6

Stroud, Caleb Zachary. "Implementing Differential Privacy for Privacy Preserving Trajectory Data Publication in Large-Scale Wireless Networks." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/84548.

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Wireless networks collect vast amounts of log data concerning usage of the network. This data aids in informing operational needs related to performance, maintenance, etc., but it is also useful for outside researchers in analyzing network operation and user trends. Releasing such information to these outside researchers poses a threat to privacy of users. The dueling need for utility and privacy must be addressed. This thesis studies the concept of differential privacy for fulfillment of these goals of releasing high utility data to researchers while maintaining user privacy. The focus is spe

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7

Huang, Zhengli. "Privacy and utility analysis of the randomization approach in Privacy-Preserving Data Publishing." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2008. http://wwwlib.umi.com/cr/syr/main.

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8

Yang, Cao. "Rigorous and Flexible Privacy Protection Framework for Utilizing Personal Spatiotemporal Data." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225733.

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9

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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10

"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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11

Iftikhar, Masooma. "Privacy-Preserving Data Publishing." Phd thesis, 2022. http://hdl.handle.net/1885/272877.

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With the advances of data analytics, preserving privacy in publishing data about individuals becomes an important task. The data publishing process includes two phases: (i) data collection phase, and (ii) data publishing phase. In the data collection phase companies, organizations, and government agencies collect data from individuals through different means (such as surveys, polls, and questionnaires). Subsequently, in the data publishing phase, the data publisher or data holder publishes the collected data and information for analysis and research purposes which are later used to inform poli

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12

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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13

HSIAO, MEI-HUI, and 蕭美慧. "Privacy-Preserving Data Publishing with Missing Values." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7t7u9u.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>105<br>Recently, privacy preserving data publishing has become an important research issue. Over the past few years, although there have been many different privacy preserving data anonymization methods proposed by the researchers, all of them are dealing with non-missing data. However, in the real world most published data contain missing values. None of contemporary work notices this problem and investigates the effect of missing values to privacy preserving data publishing. 　　The aim of this research was to discuss the impact of missing values to current privacy

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14

Li, Yidong. "Preserving privacy in data publishing and analysis." Thesis, 2011. http://hdl.handle.net/2440/68556.

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As data collection and storage techniques being greatly improved, data analysis is becoming an increasingly important issue in many business and academic collaborations that enhances their productivity and competitiveness. Multiple techniques for data analysis, such as data mining, business intelligence, statistical analysis and predictive analytics, have been developed in different science, commerce and social science domains. To ensure quality data analysis, effective information sharing between organizations becomes a vital requirement in today’s society. However, the shared data often cont

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15

WANG, CHIEH-TENG, and 王介騰. "Privacy Preserving Anonymity for Periodical SRS Data Publishing." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/16278646066845717875.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>104<br>In recent years, many countries have built their spontaneous reporting systems to collect adverse drug events for ADR detection and analysis, e.g., the FDA Adverse Event Reporting System (FAERS). The SRS data are provided to the researchers, even open to the public, to foster the research of ADR. Normally, SRS data contains personal information and some private value such as indication. Thus, it is necessary to de-identify the SRS data for prevent the disclosure of individual privacy before it is published. However, researchers have pointed out that it is no

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16

HSU, KUANG-YUNG, and 許絖詠. "Privacy-Preserving SRS Data Publishing with Missing Values." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/vy754k.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>106<br>In recent years, many countries have established Spontaneous Reporting System (SRS) for the detection and analysis of adverse drug reactions (ADRs), such as the US Food and Drug Administration's Adverse Event Reporting System (FAERS). These SRS data usually contain sensitive personal privacy information. In order to prevent personal privacy leakage, the data must be de-identified and processed by some Privacy Protection Data Publishing (PPDP) before being published. Although many scholars have proposed various privacy protection models, they overlooked char

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17

Al-Hussaeni, Khalil. "Preserving Data Privacy and Information Usefulness for RFID Data Publishing." Thesis, 2009. http://spectrum.library.concordia.ca/976457/1/MR63082.pdf.

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Radio-Frequency IDentification (RFID) is an emerging technology that employs radio waves to identify, locate, and track objects. RFID technology has wide applications in many areas including manufacturing, healthcare, and transportation. However, the manipulation of uniquely identifiable objects gives rise to privacy concerns for the individuals carrying these objects. Most previous works on privacy-preserving RFID technology, such as EPC re-encryption and killing tags, have focused on the threats caused by the physical RFID tags in the data collection phase, but these techniques cannot addres

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18

Yang, Duen-Chuan, and 楊敦筌. "Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/53521333395278780908.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>103<br>In recent years, spontaneous reporting systems (SRSs) have been widely established to collect adverse drug events (ADEs) for ADR detection and analysis, e.g., the FDA Adverse Event Reporting System (FAERS). Usually, SRS data contain sensitive personal health information that should be protected to prevent the identification of individuals, raising the need of anonymizing the raw data before being published, namely privacy-preserving data publishing (PPDP). Although much work has been done on PPDP, very few studies have focused on protecting privacy of SRS da

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19

"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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20

CHANG, YU-HSIANG, and 張煜祥. "Privacy-Preserving High Dimensional Data Publishing Mechanism Meets K-Anonymity and Differential Privacy." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7n3k93.

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21

Khokhar, Rashid Hussain. "Quantifying the Costs and Benefits of Privacy-Preserving Health Data Publishing." Thesis, 2013. http://spectrum.library.concordia.ca/977136/1/Khokhar_MASc_S2013.pdf.

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Cost-benefit analysis is required for making good business decision. This analysis is crucial in the field of privacy-preserving data publishing. In the economic trade of data privacy and utility, organization has the obligation to respect privacy of individuals. They intend to maximize the utility in order to earn revenue and also aim to achieve the acceptable level of privacy. In this thesis, we study the privacy and utility trade-offs and propose an analytical cost model which can help organization in better decision making subject to sharing customer data with another party. We examine the

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22

"Privacy preserving data publishing: an expected gain model with negative association immunity." 2012. http://library.cuhk.edu.hk/record=b5549584.

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隱私保護是許多應用(特別是和人們有關的)要面對的重要問題。在隱私保護數據發布之研究中，我們探討如何在個人隱私不會被侵犯之情況下發布一個包含個人資料之數據庫，而此數據庫仍包含有用的信息以供研究或其他數據分析之用。<br>本論文著重於隱私保護數據發布之隱私模型及算法。我們首先提出一個預期收益模型，以確認發布一個數據庫會否侵犯個人隱私。預期收益模型符合我們在本論文中提出的六個關於量化私人信息之公理，而第六條公理還會以社會心理學之角度考慮人為因素。而且，這模型考慮敵意信息收集人在發布數據庫之中所得到的好處。所以這模型切實反映出敵意信息收集人利用這些好處而獲得利益，而其他隱私模型並沒有考慮這點。然後，我們還提出了一個算法來生成符合預期收益模型之發布數據庫。我們亦進行了一些包含現實數據庫之實驗來表示出這算法是現實可行的。在那之後，我們提出了一個敏感值抑制算法，使發布數據庫能對負向關聯免疫，而負向關聯是前景/背景知識攻擊之一種。我們亦進行了一些實驗來表示出我們只需要抑制平均數個百份比之敏感值就可以令一個發佈數據庫對負向關聯免疫。最後，我們探討在分散環境之下之隱私保護數據發布，這代表有兩個或以上的數據庫持有人分別生成不同但有關之發布數據庫。我們提出一個在分散環境下可用的相異L多樣性的隱私模型和一個算法來生成符合此模型之發布數據庫。我們亦進行了一些實驗來表示出這算法是現實可行的。<br>Privac

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23

Zhang, X. "Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing." Thesis, 2014. http://hdl.handle.net/10453/30324.

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University of Technology, Sydney. Faculty of Engineering and Information Technology.<br>Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to current IT industry and research communities while posing significant challenges on them as well. The massive increase in computing power and data storage capacity provisioned by the cloud and the advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals by orders of magnitude. A major obstacle to the adoption of cloud computing i

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24

Ho, Shih-Han, and 何是翰. "Maximizing Discriminability on Dynamic Attributes for Privacy-Preserving Data Publishing Using K-Anonymity." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w2cpcb.

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碩士<br>國立中興大學<br>電機工程學系所<br>107<br>There are increasing demands on open data for scientific, medical, and social applications. Open data is a new trend and more data are being released for data mining and decision-making. To avoid the leakage of personal privacy caused by the release of data, data must be processed through privacy protection methods before being released. Since the optimization of privacy preserving models like K-anonymity and L-diversity are NP problems, most previous privacy preserving methods trade off privacy preserving and data utility by designing heuristic algorithms to

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