Surapon Riyana
Noppamas Riyana
ABSTRACT
RFID is a smart label technology that is used in several real-life applications such as inventory management, asset tracking, personnel tracking, controlling access to restricted areas, ID badging, supply chain management, counterfeit prevention (e.g., in the pharmaceutical industry), and smart farming. Generally, the data collection of RFIDs consists of the users’ visited locations and their visiting time, so called as trajectory datasets. Aside from applications, trajectory datasets can also be released for public use. For this reason, they could lead to being privacy violation issues. To address these issues in trajectory datasets, LKC-Privacy is proposed. Unfortunately, in this work, we demonstrate that LKC-Privacy still has a serious vulnerability that must be improved. To rid the demonstrated vulnerability of LKC-Privacy, a privacy preservation model is proposed in this work. Furthermore, the proposed mode is evaluated by extensive experiments. From the experimental results, they indicate that the proposed model is highly secure and more efficient than LKC-Privacy.
- Benjamin C. M. Fung, Ming Cao, Bipin C. Desai, and Heng Xu. 2009. Privacy Protection for RFID Data. In Proceedings of the 2009 ACM Symposium on Applied Computing (Honolulu, Hawaii) (SAC ’09). Association for Computing Machinery, New York, NY, USA, 1528–1535. https://doi.org/10.1145/1529282.1529626Google Scholar
Digital Library
- A. Juels. 2006. RFID security and privacy: a research survey. IEEE Journal on Selected Areas in Communications 24, 2(2006), 381–394. https://doi.org/10.1109/JSAC.2005.861395Google ScholarDigital Library
- Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. 2007. Trajectory Clustering: A Partition-and-Group Framework. In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (Beijing, China) (SIGMOD ’07). Association for Computing Machinery, New York, NY, USA, 593–604. https://doi.org/10.1145/1247480.1247546Google ScholarDigital Library
- A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. 2006. L-diversity: privacy beyond k-anonymity. In 22nd International Conference on Data Engineering (ICDE’06). 24–24. https://doi.org/10.1109/ICDE.2006.1Google ScholarDigital Library
- Noman Mohammed, Benjamin C.M. Fung, and Mourad Debbabi. 2010. Preserving Privacy and Utility in RFID Data Publishing. Technical Report. https://spectrum.library.concordia.ca/6850/Google Scholar
- Noppamas Riyana, Surapon Riyana, Srikul Nanthachumphu, Suphannika Sittisung, and Dussadee Duangban. 2020. Privacy Violation Issues in Re-publication of Modification Datasets. In International Conference on Intelligent Computing & Optimization. Springer, 938–953.Google Scholar
- Surapon Riyana. 2021. (lp1, ..., lpn)-Privacy: privacy preservation models for numerical quasi-identifiers and multiple sensitive attributes. Journal of Ambient Intelligence and Humanized Computing (jan 2021). https://doi.org/10.1007/s12652-020-02715-3Google ScholarCross Ref
- Surapon Riyana, Nattapon Harnsamut, Uratcha Sadjapong, Srikul Nanthachumphu, and Noppamas Riyana. 2021. Privacy Preservation for Continuous Decremental Data Publishing. In Image Processing and Capsule Networks, Joy Iong-Zong Chen, João Manuel R. S. Tavares, Subarna Shakya, and Abdullah M. Iliyasu (Eds.). Springer International Publishing, Cham, 233–243.Google Scholar
- S. Riyana, N. Harnsamut, T. Soontornphand, and J. Natwichai. 2015. (k, e)-Anonymous for Ordinal Data. In 2015 18th International Conference on Network-Based Information Systems. 489–493. https://doi.org/10.1109/NBiS.2015.118Google ScholarDigital Library
- Surapon Riyana, Srikul Nanthachumphu, and Noppamas Riyana. 2020. Achieving Privacy Preservation Constraints in Missing-Value Datasets. SN Comput. Sci. 1, 4 (2020), 227. https://doi.org/10.1007/s42979-020-00241-9Google ScholarDigital Library
- Surapon Riyana and Juggapong Natwichai. 2018. Privacy preservation for recommendation databases. Serv. Oriented Comput. Appl. 12, 3-4 (2018), 259–273. https://doi.org/10.1007/s11761-018-0248-yGoogle ScholarDigital Library
- Surapon Riyana, Noppamas Riyana, and Srikul Nanthachumphu. 2017. Enhanced (k, e)-anonymous for categorical data. In Proceedings of the 6th International Conference on Software and Computer Applications. 62–67.Google ScholarDigital Library
- Surapon Riyana, Noppamas Riyana, and Srikul Nanthachumphu. 2021. An Effective and Efficient Heuristic Privacy Preservation Algorithm for Decremental Anonymization Datasets. In Image Processing and Capsule Networks, Joy Iong-Zong Chen, João Manuel R. S. Tavares, Subarna Shakya, and Abdullah M. Iliyasu (Eds.). Springer International Publishing, Cham, 244–257.Google Scholar
- Surapon Riyana, Noppamas Riyana, and Warunsiri Sujinda. 2021. An Anatomization Model for Farmer Data-Collections. SN Computer Science (2021).Google Scholar
- J. A. Shamsi and M. A. Khojaye. 2018. Understanding Privacy Violations in Big Data Systems. IT Professional 20, 3 (2018), 73–81. https://doi.org/10.1109/MITP.2018.032501750Google ScholarDigital Library
- Latanya Sweeney. 2002. k-Anonymity: A Model for Protecting Privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10, 5 (Oct. 2002), 557–570. https://doi.org/10.1142/S0218488502001648Google ScholarDigital Library
- Takako Takai-Igarashi, Kengo Kinoshita, Masao Nagasaki, Soichi Ogishima, Naoki Nakamura, Sachiko Nagase, Satoshi Nagaie, Tomo Saito, Fuji Nagami, Naoko Minegishi, Yoichi Suzuki, Kichiya Suzuki, Hiroaki Hashizume, Shinichi Kuriyama, Atsushi Hozawa, Nobuo Yaegashi, Shigeo Kure, Gen Tamiya, Yoshio Kawaguchi, and Masayuki Tamamoto. 2017. Security controls in an integrated Biobank to protect privacy in data sharing: Rationale and study design. BMC Medical Informatics and Decision Making 17 (07 2017). https://doi.org/10.1186/s12911-017-0494-5Google ScholarCross Ref
- Manolis Terrovitis, Nikos Mamoulis, and Panos Kalnis. 2008. Privacy-Preserving Anonymization of Set-Valued Data. Proc. VLDB Endow. 1, 1 (Aug. 2008), 115–125. https://doi.org/10.14778/1453856.1453874Google ScholarDigital Library
- Z. Tu, F. Xu, Y. Li, P. Zhang, and D. Jin. 2018. A New Privacy Breach: User Trajectory Recovery From Aggregated Mobility Data. IEEE/ACM Transactions on Networking 26, 3 (2018), 1446–1459. https://doi.org/10.1109/TNET.2018.2829173Google ScholarDigital Library
- Raymond Chi-Wing Wong, Jiuyong Li, Ada Wai-Chee Fu, and Ke Wang. 2006. (alpha, k)-Anonymity: An Enhanced k-Anonymity Model for Privacy Preserving Data Publishing(KDD ’06). Association for Computing Machinery, New York, NY, USA, 754–759. https://doi.org/10.1145/1150402.1150499Google ScholarDigital Library
- Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin. 2017. Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1241–1250. https://doi.org/10.1145/3038912.3052620Google ScholarDigital Library
- C. Yin, J. Xi, R. Sun, and J. Wang. 2018. Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things. IEEE Transactions on Industrial Informatics 14, 8 (2018), 3628–3636. https://doi.org/10.1109/TII.2017.2773646Google ScholarCross Ref
- Yu Zheng. 2015. Trajectory Data Mining: An Overview. ACM Trans. Intell. Syst. Technol. 6, 3, Article 29 (May 2015), 41 pages. https://doi.org/10.1145/2743025Google ScholarDigital Library
Index Terms
- A Privacy Preservation Model for RFID Data-Collections is Highly Secure and More Efficient than LKC-Privacy
Recommendations
Privacy Preservation Techniques for Sequential Data Releasing
IAIT '21: Proceedings of the 12th International Conference on Advances in Information TechnologyPrivacy violation is a serious issue that must be considered when datasets are released for public use. To address this issue, a well-known privacy preservation model, l-Diversity, is proposed. Unfortunately, l-Diversity is generally proposed to ...
Privacy protection for RFID data
SAC '09: Proceedings of the 2009 ACM symposium on Applied ComputingRadio Frequency IDentification (RFID) is a technology of automatic object identification. Retailers and manufacturers have created compelling business cases for deploying RFID in their supply chains. Yet, the uniquely identifiable objects pose a privacy ...
Achieving Privacy Preservation Constraints in Missing-Value Datasets
AbstractPrivacy violation issues must be taken into consideration when datasets are released for public use. To address these issues, there are various anonymization models to be proposed, e.g., k-anonymity, l-diversity, and t-closeness. However, these ...
Comments