Relevant bibliographies by topics / Privacy Preserving Data Mining (PPDM)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Privacy Preserving Data Mining (PPDM)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Privacy Preserving Data Mining (PPDM)"

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Radhika, D., and D. Aruna Kumari. "Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4524–32. https://doi.org/10.11591/ijece.v8i6.pp4524-4532.

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Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to
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Radhika, D., and D. Aruna Kumari. "Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4524. http://dx.doi.org/10.11591/ijece.v8i6.pp4524-4532.

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Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to
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Wang, Binli, and Yanguang Shen. "Study on distributed privacy preserving data mining." World Journal of Engineering 11, no. 2 (2014): 163–70. http://dx.doi.org/10.1260/1708-5284.11.2.163.

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Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points ou
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Doğuç, Özge. "Data Mining Applications in Banking Sector While Preserving Customer Privacy." Emerging Science Journal 6, no. 6 (2022): 1444–54. http://dx.doi.org/10.28991/esj-2022-06-06-014.

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In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and
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Et. al., P. Rajendra Prasad,. "Efficient Model for Privacy Preserving Classification Of Data Streams." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1475–81. http://dx.doi.org/10.17762/turcomat.v12i2.1376.

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Privacy preserving data mining has become progressively mainstream since it permits sharing of privacy delicate data for examination purposes .So individuals have gotten progressively reluctant to share their data, regularly bringing about people either declining to share their data or giving inaccurate data. As of late, privacy preserving data mining has been concentrated broadly, on account of the wide multiplication of delicate data on the web. Data Mining manages programmed extraction of already obscure examples from a lot of data sets. These data sets ordinarily contain touchy individual
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Patel, Mohnish, Prashant Richariya, and Anurag Shrivastava. "A review paper on Privacy-Preserving Data Mining." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 09 (2013): 296–99. https://doi.org/10.5281/zenodo.14613361.

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Data mining technology help us in extraction of 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 private information about individuals, businesses and organizations has to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important issue in current years. This paper we propose an evolutionary privacy-preserving data mining technology to find appropriate method to perform secure transactions into a database 
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Gunawan, Dedi. "Classification of Privacy Preserving Data Mining Algorithms: A Review." Jurnal Elektronika dan Telekomunikasi 20, no. 2 (2020): 36. http://dx.doi.org/10.14203/jet.v20.36-46.

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Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the da
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Raj, Diana Judith Irudaya, Vijay Sai Radhakrishnan, Manyam Rajasekhar Reddy, Natarajan Senthil Selvan, Balasubramanian Elangovan, and Manikandan Ganesan. "The Projection-Based Data Transformation Approach for Privacy Preservation in Data Mining." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 15969–74. http://dx.doi.org/10.48084/etasr.7969.

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Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a bal
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Utomo, Wiranto Herry, Rosalina Rosalina, and Afriyadi Afriyadi. "Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1929. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1929-1937.

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The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-pres
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Wiranto, Herry Utomo Rosalina Rosalina Afriyadi Afriyadi. "Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1929–37. https://doi.org/10.11591/ijeecs.v36.i3.pp1929-1937.

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The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-pres
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Dissertations / Theses on the topic "Privacy Preserving Data Mining (PPDM)"

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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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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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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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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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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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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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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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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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Books on the topic "Privacy Preserving Data Mining (PPDM)"

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Aggarwal, Charu C., and Philip S. Yu, eds. Privacy-Preserving Data Mining. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5.

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Vaidya, Jaideep. Privacy preserving data mining. Springer, 2006.

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S, Yu Philip, and SpringerLink (Online service), eds. Privacy-Preserving Data Mining: Models and Algorithms. Springer Science+Business Media, LLC, 2008.

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Privacy Preserving Data Mining. Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-29489-6.

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Vaidya, Jaideep, Christopher W. Clifton, and Yu Michael Zhu. Privacy Preserving Data Mining. Springer, 2010.

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Wai-Chee, Ada, and Raymond Chi-Wing Wong. Privacy-Preserving Data Publishing. Springer International Publishing AG, 2010.

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Wong, Raymond Chi-Wing, and Ada Wai-Chee Fu. Privacy-Preserving Data Publishing: An Overview. Morgan & Claypool Publishers, 2010.

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Wong, Raymond Chi-Wing, and Ada Wai-Chee Fu. Privacy-Preserving Data Publishing: An Overview. Morgan & Claypool Publishers, 2010.

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Yu, Philip S., Ke Wang, Benjamin C. M. Fung, and Ada Wai-Chee Fu. Introduction to Privacy-Preserving Data Publishing. Taylor & Francis Group, 2010.

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Privacy Preserving Data Mining (Advances in Information Security). Springer, 2005.

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Book chapters on the topic "Privacy Preserving Data Mining (PPDM)"

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S. Hirschprung, Ron. "Privacy-Preserving Data Mining (PPDM)." In Machine Learning for Data Science Handbook. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24628-9_38.

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Aggarwal, Charu C. "Privacy-Preserving Data Mining." In Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_20.

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Yin, Yong, Ikou Kaku, Jiafu Tang, and JianMing Zhu. "Privacy-preserving Data Mining." In Data Mining. Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-338-1_6.

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Kumari, D. Aruna, K. Rajasekhara Rao, and M. Suman. "Privacy Preserving Data Mining." In ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03095-1_55.

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Clifton, Chris. "Privacy-Preserving Data Mining." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_270-2.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Privacy-Preserving Data Mining." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_667.

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Brankovic, Ljiljana, Md Zahidul Islam, and Helen Giggins. "Privacy-Preserving Data Mining." In Security, Privacy, and Trust in Modern Data Management. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-69861-6_11.

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Clifton, Chris. "Privacy-Preserving Data Mining." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_270.

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Clifton, C., M. Kantarcıoğlu, and J. Vaidya. "Privacy-Preserving Data Mining." In Foundations and Advances in Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11362197_11.

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Lindell, Yehuda, and Benny Pinkas. "Privacy Preserving Data Mining." In Advances in Cryptology — CRYPTO 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44598-6_3.

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Conference papers on the topic "Privacy Preserving Data Mining (PPDM)"

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Kale, Dattatray Raghunath, Tushar S. Mane, Amar Buchade, Prashant B. Patel, Lalit Kumar Wadhwa, and Rajendra G. Pawar. "Federated Learning for Privacy-Preserving Data Mining." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828741.

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Joshi, Bineet Kumar, and Bansidhar Joshi. "Preserving Privacy in Data Mining: A Comparative Study." In 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies (HISET). IEEE, 2024. http://dx.doi.org/10.1109/hiset61796.2024.00047.

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Josphineleela, R., S. Kaliapp, L. Natrayan, and Ashish Garg. "Big Data Security through Privacy – Preserving Data Mining (PPDM): A Decentralization Approach." In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2023. http://dx.doi.org/10.1109/icears56392.2023.10085646.

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Agrawal, Rakesh, and Ramakrishnan Srikant. "Privacy-preserving data mining." In the 2000 ACM SIGMOD international conference. ACM Press, 2000. http://dx.doi.org/10.1145/342009.335438.

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Zhan, Justin. "Privacy Preserving Collaborative Data Mining." In 2007 IEEE Intelligence and Security Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isi.2007.379472.

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Beck, Martin, and Michael Marhofer. "Privacy-preserving data mining demonstrator." In 2012 16th International Conference on Intelligence in Next Generation Networks (ICIN): Realising the Power of the Network. IEEE, 2012. http://dx.doi.org/10.1109/icin.2012.6376028.

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Zhan, Justin. "Quantifying Privacy for Privacy Preserving Data Mining." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368935.

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Gan, Wensheng, Jerry Chun-Wei, Han-Chieh Chao, Shyue-Liang Wang, and Philip S. Yu. "Privacy Preserving Utility Mining: A Survey." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622405.

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Jagannathan, Geetha, and Rebecca Wright. "Privacy-Preserving Data Imputation." In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.134.

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Monreale, Anna, and Wendy Hui Wang. "Privacy-Preserving Outsourcing of Data Mining." In 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2016. http://dx.doi.org/10.1109/compsac.2016.169.

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Reports on the topic "Privacy Preserving Data Mining (PPDM)"

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Zhan, Zhijun, and LiWu Chang. Privacy-Preserving Collaborative Data Mining. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada464602.

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