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Investigation on Privacy-Preserving Techniques For Personal Data

Published: 21 August 2021 Publication History

Abstract

Privacy protection technology has become a crucial part of almost every existing cross-data analysis application. The privacy-preserving technique allows sharing sensitive personal information and preserves the users' privacy. This new trend influences data collection results by improving the analytical accuracy, increasing the number of participants, and better understand the participants' environments. Herein, collecting these personal data is significant to many advantageous applications such as health monitoring. Nevertheless, these applications encounter real privacy threats and concerns about handling personal information. This paper aims to determine privacy-preserving personal data mining technologies and analyze these technologies' advantages and shortcomings. Our purpose is to provide an in-depth understanding of personal data privacy and highlight important viewpoints, existing challenges, and future research directions.

References

[1]
Abdulatif Alabdulatif, Ibrahim Khalil, and Xun Yi. 2020. Towards secure big data analytic for cloud-enabled applications with fully homomorphic encryption. J. Parallel and Distrib. Comput., Vol. 137 (2020), 192--204.
[2]
Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al. 2017. Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, Vol. 13, 5 (2017), 1333--1345.
[3]
Fabrice Benhamouda, Shai Halevi, and Tzipora Halevi. 2019. Supporting private data on hyperledger fabric with secure multiparty computation. IBM Journal of Research and Development, Vol. 63, 2/3 (2019), 3--1.
[4]
Elisa Bertino, Igor Nai Fovino, and Loredana Parasiliti Provenza. 2005. A framework for evaluating privacy preserving data mining algorithms. Data Mining and Knowledge Discovery, Vol. 11, 2 (2005), 121--154.
[5]
Simon Blackburn. 2005. The Oxford dictionary of philosophy .OUP Oxford.
[6]
R Bocu and C Costache. 2018. A homomorphic encryption-based system for securely managing personal health metrics data. IBM Journal of Research and Development, Vol. 62, 1 (2018), 1--1.
[7]
Jan Camenisch and Anja Lehmann. 2017. Privacy for Distributed Databases via (Un) linkable Pseudonyms. IACR Cryptol. ePrint Arch., Vol. 2017 (2017), 22.
[8]
Mahawaga Arachchige Pathum Chamikara, Peter Bertók, Dongxi Liu, Seyit Camtepe, and Ibrahim Khalil. 2019. An efficient and scalable privacy preserving algorithm for big data and data streams. Computers & Security, Vol. 87 (2019), 101570.
[9]
Long Cheng, Fang Liu, and Danfeng Yao. 2017. Enterprise data breach: causes, challenges, prevention, and future directions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 7, 5 (2017), e1211.
[10]
Minh-Son Dao, Morten Fjeld, Filip Biljecki, Uraz Yavanoglu, and Mianxiong Dong. 2020. ICDAR'20: Intelligent Cross-Data Analysis and Retrieval. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 580--581.
[11]
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference. Springer, 265--284.
[12]
Can Eyupoglu, Muhammed Ali Aydin, Abdul Halim Zaim, and Ahmet Sertbas. 2018. An efficient big data anonymization algorithm based on chaos and perturbation techniques. Entropy, Vol. 20, 5 (2018), 373.
[13]
Dennis RE Gnad, Jonas Krautter, and Mehdi B Tahoori. 2019. Leaky noise: new side-channel attack vectors in mixed-signal IoT devices. IACR Transactions on Cryptographic Hardware and Embedded Systems (2019), 305--339.
[14]
Mehmet Emre Gursoy, Acar Tamersoy, Stacey Truex, Wenqi Wei, and Ling Liu. 2019. Secure and utility-aware data collection with condensed local differential privacy. IEEE Transactions on Dependable and Secure Computing (2019).
[15]
Adnan Gutub and Khaled Alaseri. 2019. Hiding shares of counting-based secret sharing via Arabic text steganography for personal usage. Arabian Journal for Science and Engineering (2019), 1--26.
[16]
Rob Hall, Stephen E Fienberg, and Yuval Nardi. 2011. Secure multiple linear regression based on homomorphic encryption. Journal of Official Statistics, Vol. 27, 4 (2011), 669.
[17]
Rafik Hamza, Zheng Yan, Khan Muhammad, Paolo Bellavista, and Faiza Titouna. 2020. A privacy-preserving cryptosystem for IoT E-healthcare. Information Sciences, Vol. 527 (2020), 493--510.
[18]
Hamed Hellaoui, Mouloud Koudil, and Abdelmadjid Bouabdallah. 2020. Energy-efficiency in security of 5G-based IoT: An end-to-end adaptive approach. IEEE Internet of Things Journal (2020).
[19]
Xuyang Jing, Zheng Yan, and Witold Pedrycz. 2018. Security data collection and data analytics in the Internet: A survey. IEEE Communications Surveys & Tutorials, Vol. 21, 1 (2018), 586--618.
[20]
MohammadReza Keyvanpour and Somayyeh Seifi Moradi. 2011. Classification and evaluation the privacy preserving data mining techniques by using a data modification-based framework. arXiv preprint arXiv:1105.1945 (2011).
[21]
Maryam Kiabod, Mohammad Naderi Dehkordi, and Behrang Barekatain. 2019. TSRAM: A time-saving k-degree anonymization method in social network. Expert Systems with Applications, Vol. 125 (2019), 378--396.
[22]
Jong Wook Kim, Jong Hyun Lim, Su Mee Moon, and Beakcheol Jang. 2019. Collecting health lifelog data from smartwatch users in a privacy-preserving manner. IEEE Transactions on Consumer Electronics, Vol. 65, 3 (2019), 369--378.
[23]
Jing Li, Xiaohui Kuang, Shujie Lin, Xu Ma, and Yi Tang. 2020. Privacy preservation for machine learning training and classification based on homomorphic encryption schemes. Information Sciences, Vol. 526 (2020), 166--179.
[24]
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. 2007. t-closeness: Privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 106--115.
[25]
Xueyun Li, Zheng Yan, and Peng Zhang. 2014. A review on privacy-preserving data mining. In 2014 IEEE International Conference on Computer and Information Technology. IEEE, 769--774.
[26]
Yang Lu, Jiguo Li, and Yichen Zhang. 2019. Secure channel free certificate-based searchable encryption withstanding outside and inside keyword guessing attacks. IEEE Transactions on Services Computing (2019).
[27]
Deborah Lupton. 2014. Self-tracking cultures: towards a sociology of personal informatics. In Proceedings of the 26th Australian computer-human interaction conference on designing futures: The future of design. 77--86.
[28]
Chuan Ma, Jun Li, Ming Ding, Howard H Yang, Feng Shu, Tony QS Quek, and H Vincent Poor. 2020. On Safeguarding Privacy and Security in the Framework of Federated Learning. IEEE Network (2020).
[29]
Qiang Ma, Shanfeng Zhang, Tong Zhu, Kebin Liu, Lan Zhang, Wenbo He, and Yunhao Liu. 2016. PLP: Protecting location privacy against correlation analyze attack in crowdsensing. IEEE transactions on mobile computing, Vol. 16, 9 (2016), 2588--2598.
[30]
Martin M Merener. 2012. Theoretical Results on De-Anonymization via Linkage Attacks. Trans. Data Priv., Vol. 5, 2 (2012), 377--402.
[31]
Divya G Nair, VP Binu, and G Santhosh Kumar. 2014. An effective private data storage and retrieval system using secret sharing scheme based on secure multi-party computation. In 2014 International Conference on Data Science & Engineering (ICDSE). IEEE, 210--214.
[32]
Arvind Narayanan and Vitaly Shmatikov. 2006. How to break anonymity of the netflix prize dataset. arXiv preprint cs/0610105 (2006).
[33]
Ngoc-Thanh Nguyen, Minh-Son Dao, and Koji Zettsu. 2020. Leveraging 3D-Raster-Images and DeepCNN with Multi-source Urban Sensing Data for Traffic Congestion Prediction. In International Conference on Database and Expert Systems Applications. Springer, 396--406.
[34]
Akash Suresh Patil, Rafik Hamza, Alzubair Hassan, Nan Jiang, Hongyang Yan, and Jin Li. 2020. Efficient privacy-preserving authentication protocol using PUFs with blockchain smart contracts. Computers & Security, Vol. 97 (2020), 101958.
[35]
Lianyong Qi, Xuyun Zhang, Shancang Li, Shaohua Wan, Yiping Wen, and Wenwen Gong. 2020. Spatial-temporal data-driven service recommendation with privacy-preservation. Information Sciences, Vol. 515 (2020), 91--102.
[36]
Jyri Rajam"aki and Jussi Simola. 2019. How to apply privacy by design in OSINT and big data analytics. In ECCWS 2019 18th European Conference on Cyber Warfare and Security. Academic Conferences and publishing limited, 364.
[37]
Aobakwe Senosi and George Sibiya. 2017. Classification and evaluation of privacy preserving data mining: a review. In 2017 IEEE AFRICON. IEEE, 849--855.
[38]
Manish Sharma, Atul Chaudhary, Manish Mathuria, and Shalini Chaudhary. 2013. A review study on the privacy preserving data mining techniques and approaches. International Journal of Computer Science and Telecommunications, Vol. 4, 9 (2013), 42--46.
[39]
Cristian-Alexandru Staicu and Michael Pradel. 2019. Leaky images: Targeted privacy attacks in the web. In 28th USENIX Security Symposium (USENIX Security 19). 923--939.
[40]
Peng Tang, Xiang Cheng, Sen Su, Rui Chen, and Huaxi Shao. 2019. Differentially Private Publication of Vertically Partitioned Data. IEEE Transactions on Dependable and Secure Computing (2019).
[41]
Meilof Veeningen, Supriyo Chatterjea, Anna Zsófia Horváth, Gerald Spindler, Eric Boersma, Peter van der SPEK, Onno van der GALIËN, Job Gutteling, Wessel Kraaij, and Thijs Veugen. 2018. Enabling Analytics on Sensitive Medical Data with Secure Multi-Party Computation. In MIE. 76--80.
[42]
Vassilios S Verykios, Elisa Bertino, Igor Nai Fovino, Loredana Parasiliti Provenza, Yucel Saygin, and Yannis Theodoridis. 2004. State-of-the-art in privacy preserving data mining. ACM Sigmod Record, Vol. 33, 1 (2004), 50--57.
[43]
Junyi Wei, Yicheng Zhang, Zhe Zhou, Zhou Li, and Mohammad Abdullah Al Faruque. 2020. Leaky dnn: Stealing deep-learning model secret with gpu context-switching side-channel. In 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 125--137.
[44]
Xiang Wu, Yongting Zhang, Aming Wang, Minyu Shi, Huanhuan Wang, and Lian Liu. 2020. MNSSp3: Medical big data privacy protection platform based on Internet of things. NEURAL COMPUTING & APPLICATIONS (2020).
[45]
Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, and Yong Ren. 2014. Information security in big data: privacy and data mining. Ieee Access, Vol. 2 (2014), 1149--1176.

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  • (2024)Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing TechnologiesJournal on Computing and Cultural Heritage 10.1145/363347716:4(1-18)Online publication date: 8-Jan-2024
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  • (2024)Discovering Personally Identifiable Information in Textual Data - A Case Study with Automated Concatenation of EmbeddingsAdvanced Information Networking and Applications10.1007/978-3-031-57916-5_13(145-158)Online publication date: 9-Apr-2024
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cover image ACM Conferences
ICDAR '21: Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval
August 2021
72 pages
ISBN:9781450385299
DOI:10.1145/3463944
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 August 2021

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Author Tags

  1. data collection
  2. personal data
  3. privacy-preserving
  4. requirements
  5. security

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Cited By

View all
  • (2024)Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing TechnologiesJournal on Computing and Cultural Heritage 10.1145/363347716:4(1-18)Online publication date: 8-Jan-2024
  • (2024)Privacy-Preserving and Efficient Border Surveillance System using Advanced Deep Learning and Cryptographic Techniques2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714893(733-736)Online publication date: 3-Oct-2024
  • (2024)Discovering Personally Identifiable Information in Textual Data - A Case Study with Automated Concatenation of EmbeddingsAdvanced Information Networking and Applications10.1007/978-3-031-57916-5_13(145-158)Online publication date: 9-Apr-2024
  • (2022)Towards Secure Big Data Analysis via Fully Homomorphic Encryption AlgorithmsEntropy10.3390/e2404051924:4(519)Online publication date: 6-Apr-2022
  • (2022)A Survey of Intellectual Property Rights Protection in Big Data ApplicationsAlgorithms10.3390/a1511041815:11(418)Online publication date: 8-Nov-2022
  • (2022)Systematic Analysis of Predictive Modeling Methods in Stock MarketsInternational Research Journal of Computer Science10.26562/irjcs.2022.v0911.019:11(377-385)Online publication date: 2022
  • (2022)Comprehensive Analysis of Privacy Preserving Data Mining Algorithms for Future Develop TrendsInternational Research Journal of Computer Science10.26562/irjcs.2022.v0910.019:10(367-374)Online publication date: 2022

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