The Projection-Based Data Transformation Approach for Privacy Preservation in Data Mining
Received: 29 May 2024 | Revised: 17 June 2024 | Accepted: 24 June 2024 | Online: 4 July 2024
Corresponding author: Manikandan Ganesan
Abstract
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 balance between privacy protection and data utility. By employing PPDM techniques, organizations can perform safe and private data analysis, protecting sensitive information while deriving valuable insights from the data. The current paper uses geometric transformation-based projection techniques such as perspective projection, isometric projection, cabinet projection, and cavalier projection to protect data privacy and improve data utility. The suggested technique's performance was assessed with the K-means clustering technique. The UCI repository's Bank Marketing dataset was used to verify the error rate of the proposed projection techniques.
Keywords:
data privacy, data mining, data transformation, projection, k-means algorithmDownloads
References
B. Karthikeyan, G. Manikandan, and V. Vaithiyanathan, "A fuzzy based approach for privacy preserving clustering," Journal of Theoretical and Applied Information Technology, vol. 32, no. 2, pp. 118–122, 2011.
G. Manikandan, N. Sairam, S. Sharmili, and S. Venkatakrishnan, "Achieving Privacy in Data Mining Using Normalization," Indian Journal of Science and Technology, vol. 6, no. 4, pp. 4268–4272, Apr. 2013.
G. Manikandan, N. Sairam, S. Jayashree, and C. Saranya, "Achieving Data Privacy in a Distributed Environment Using Geometrical Transformation," Middle-East Journal of Scientific Research, vol. 14, no. 1, pp. 107–111, 2013.
C. Saranya and G. Manikandan, "A Study on Normalization Techniques for Privacy Preserving Data Mining," International Journal of Engineering and Technology, vol. 5, no. 3, pp. 2701–2704, 2013.
G. Manikandan, N. Sairam, V. Harish, and N. Saikumar, "A substitution based approach for ensuring medical data privacy," Research Journal of Pharmaceutical, Biological and Chemical Sciences, vol. 7, no. 2, pp. 1136–1139, Jan. 2016.
G. Manikandan, N. Sairam, V. Harish, and N. Saikumar, "Survey on the use of fuzzy membership functions to ensure data privacy," Research Journal of Pharmaceutical, Biological and Chemical Sciences, vol. 7, no. 3, pp. 344–348, Jan. 2016.
D. Niranjan, G. Manikandan, N. Sairam, V. Harish, and N. Saikumar, "Ensuring privacy in data mining using neural networks," Research Journal of Pharmaceutical, Biological and Chemical Sciences, vol. 7, no. 4, pp. 1262–1267, Jan. 2016.
Y. Xiao and H. Li, "Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level," Information, vol. 11, no. 3, Mar. 2020, Art. no. 166.
P. S. Rao and S. Satyanarayana, "Privacy preserving data publishing based on sensitivity in context of Big Data using Hive," Journal of Big Data, vol. 5, no. 1, Jul. 2018, Art. no. 20.
J. Song, Q. Zhong, W. Wang, C. Su, Z. Tan, and Y. Liu, "FPDP: Flexible Privacy-Preserving Data Publishing Scheme for Smart Agriculture," IEEE Sensors Journal, vol. 21, no. 16, pp. 17430–17438, Dec. 2021.
C. S.-H. Eom, C. C. Lee, W. Lee, and C. K. Leung, "Effective privacy preserving data publishing by vectorization," Information Sciences, vol. 527, pp. 311–328, Jul. 2020.
A. S. M. T. Hasan, Q. Jiang, J. Luo, C. Li, and L. Chen, "An effective value swapping method for privacy preserving data publishing," Security and Communication Networks, vol. 9, no. 16, pp. 3219–3228, 2016.
S. A. Onashoga, B. A. Bamiro, A. T. Akinwale, and J. A. Oguntuase, "KC-Slice: A dynamic privacy-preserving data publishing technique for multisensitive attributes," Information Security Journal: A Global Perspective, vol. 26, no. 3, pp. 121–135, May 2017.
V. S. Naresh and M. Thamarai, "Privacy-preserving data mining and machine learning in healthcare: Applications, challenges, and solutions," WIREs Data Mining and Knowledge Discovery, vol. 13, no. 2, 2023, Art. no. e1490.
S. Madan and P. Goswami, "A Privacy Preserving Scheme for Big data Publishing in the Cloud using k-Anonymization and Hybridized Optimization Algorithm," in International Conference on Circuits and Systems in Digital Enterprise Technology, Kottayam, India, Dec. 2018, pp. 1–7.
B. B. Mehta and U. P. Rao, "Improved l-diversity: Scalable anonymization approach for Privacy Preserving Big Data Publishing," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1423–1430, Apr. 2022.
P. Zhao, J. Li, F. Zeng, F. Xiao, C. Wang, and H. Jiang, "ILLIA: Enabling k -Anonymity-Based Privacy Preserving Against Location Injection Attacks in Continuous LBS Queries," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1033–1042, Apr. 2018.
T. Qamar, N. Z. Bawany, and N. A. Khan, "EDAMS: Efficient Data Anonymization Model Selector for Privacy-Preserving Data Publishing," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5423–5427, Apr. 2020.
M. Atif, Z. H. Khand, S. Khan, F. Akhtar, and A. Rajput, "Storage Optimization using Adaptive Thresholding Motion Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6869–6872, Apr. 2021.
M. O. Al-Dwairi, A. Y. Hendi, and Z. A. AlQadi, "An Efficient and Highly Secure Technique to Encrypt and Decrypt Color Images," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4165–4168, Jun. 2019.
M. Rathi and A. Rajavat, "Investigations and Design of Privacy-Preserving Data Mining Technique for Secure Data Publishing," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 351–367, Jul. 2023.
N. Hrovatin, A. Tosic, M. Mrissa, and B. Kavsek, "Privacy-Preserving Data Mining on Blockchain-Based WSNs," Applied Sciences, vol. 12, no. 11, Jan. 2022, Art. no. 5646.
P. R. S. Moro, "Bank Marketing." UCI Machine Learning Repository, 2014.
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Copyright (c) 2024 Diana Judith Irudhaya Raj, Vijay Sai Radhakrishnan, Manyam Rajasekhar Reddy, Natarajan Senthil Selvan , Balasubramanian Elangovan, Manikandan Ganesan
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