{ "ok": true, "doi": "10.46243/jst.2023.v8.i12.pp23-30", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:28", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": "sensitive data is location trajectories. Spatiotemporal dataset is used in this framework, which include GPS trajectories for mobile users. The database includes k- anonymity for grouping the similar trajectories. The privacy metric for the publication of Spatiotemporal datasets is k- anonymity. The proposed algorithm is based on signature generation method, which is used to generate a key for the users in a digital manner. In signature generation method we use ECC algorithm for digital signature. We improved clustering approach and propose the 1,2,3B. Tech Student, Department of CSE (Cyber Security), Malla Reddy College of Engineering and Technology, Hyderabad, India. 4Assistant Professor, Department Of CSE (Data Science), Malla Reddy College Of Engineering and Technology, Hyderabad, India. DOI:https://doi.org/10.46243/jst.2023.v8.i12.pp23-30 15 D Nikhil Teja, Patan Abdulsalam Khan, A Sunny, R Shashi Rekha: PRIVACY PRESERVING LOCATION DATA PUBLISHING: A MACHINE LEARNING APPROACH k-means clustering. By using k-anonymity the similar trajectories were grouped and removed the dissimilar ones. Splitting techniques are used to protect the data privacy. In this paper, the proposed method is used to enhance the MLA framework to preserve the users privacy publication of Spatiotemporal datasets. The MLA framework has three algorithms: preprocessing, clustering, signature is used for the purpose of efficiency and security. The process of anonymization is to cluster. The ECC algorithm generate a private key and public key for the public users to see the personal details of a particular person. MLA algorithms are applied on real-world GPS datasets following different times and domains. Here the information loss is inversely proportional to the security level. Privacy preserving technique utilizes high quality datasets. Methods used in this paper are distribution of data, preprocessing the datasets, data mining algorithms, data hiding, signature generation, privacy preservation. The final results show the utility of the dataset and anonymization on MLA framework. LITERATURE SURVEY Data Privacy Through Optical K- Anonymization It proposes a practical method for determining an optimal anonymization of a given dataset. The optimal anonymization perturbs the input data as little as is necessary to achieve anonymity. Several different cost metrics have been proposed, through most aim in one way or another to minimize the amount of information loss results the generalization and suppression operations that are applied to produce the transformed dataset. The ability to compute optimal anonymizations let us more definitively investigate impacts of various coding techniques and problem variation on anonymization quality. It also allows to use better quantify the effectiveness of other nonoptimal methods. Winkler has proposed using simulated annealing to attack the problem but provides no evidence of its efficacy. The more theoretical side, Meyerson and Williams have recently proposed an approximation algorithm of optimal anonymization. Mondrian Multidimensional K- Anonymity Attacks can be reduced by using k- anonymity. The objective of k-anonymization technique is to protect the privacy of the every individual. The subject to this constrains, it is important that the released the data remain as “useful” as possible. This paper is a new multidimensional recoding model and a greedy algorithm for k-anonymization, an approach with several important advantages: the greedy algorithm has more efficient that proposed optimal kanonymization algorithms for single dimensional models. The greedy algorithm has the time complexity of O(nlogn), were the optimal algorithms are in worst cases. Higher quality results were produced while using greedy multidimensional algorithm than by using optimal single dimensional algorithm. Machanavajjhala Measured Anonymity by the l-diversity This paper proposed that uncertainty of linking QID with some particular sensitive values. Wang proposed to bond the confidence of inferring a particular sensitive value using one or more privacy templets specified the data provider. Wong proposed some generalization methods to simultaneously achieve k-anonymity and bond confidence. Xiao and Tao limited the breach probability, which is similar to the motion the confidence, and allowed a flexible threshold for each individual. K-anonymization for data owned by multiple parties for considered. T-closeness Privacy beyond K-anonymity and I-diversity While k-anonymity protect against the identity disclosure, it won‟t provide sufficient protection against attribute disclosure. The notion of I-diversity attempts to solve this problem by requiringthe equivalence class that has at least 1 well represented values for each sensitive attribute. 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Springer, 2018, pp. 446–459" }, { "path": "references.2.doi", "from": null, "to": "10.1109/titb.2012.2185850" }, { "path": "references.2.key", "from": null, "to": "ref3" }, { "path": "references.2.unstructured", "from": null, "to": "A. Tamersoy, G. Loukides, M. E. Nergiz, Y. Saygin, and B. Malin, “Anonymization of longitudinal electronic medical records,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 413–423, 2012" }, { "path": "references.3.doi", "from": null, "to": "10.1145/3038912.3052620" }, { "path": "references.3.key", "from": null, "to": "ref4" }, { "path": "references.3.unstructured", "from": null, "to": "F. Xu, Z. Tu, Y. Li, P. Zhang, X. Fu, and D. Jin, “Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data,” in Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017, pp. 1241–1250" }, { "path": "references.4.doi", "from": null, "to": "10.1016/j.knosys.2018.01.007" }, { "path": "references.4.key", "from": null, "to": "ref5" }, { "path": "references.4.unstructured", "from": null, "to": "Y. Dong and D. Pi, “Novel privacy-preserving algorithm based on frequent path for trajectory data publishing,” Knowledge-Based Systems, vol. 148, pp. 55–65, 2018" }, { "path": "references.5.doi", "from": null, "to": "10.1109/infocom.2017.8056979" }, { "path": "references.5.key", "from": null, "to": "ref6" }, { "path": "references.5.unstructured", "from": null, "to": "M. Gramaglia, M. Fiore, A. Tarable, and A. Banchs, “Towards privacy- preserving publishing of spatiotemporal trajectory data,” arXiv preprint arXiv:1701.02243, 2017" }, { "path": "references.6.doi", "from": null, "to": "10.1109/tkde.2017.2675420" }, { "path": "references.6.key", "from": null, "to": "ref7" }, { "path": "references.6.unstructured", "from": null, "to": "M. Terrovitis, G. Poulis, N. Mamoulis, and S. Skiadopoulos, “Local suppression and splitting techniques for privacy preserving publication of trajectories,” IEEE Trans. Knowl. Data Eng, vol. 29, no. 7, pp. 1466–1479, 2017" }, { "path": "references.7.doi", "from": null, "to": "10.1145/1503402.1503413" }, { "path": "references.7.key", "from": null, "to": "ref8" }, { "path": "references.7.unstructured", "from": null, "to": "M. E. Nergiz, M. Atzori, and Y. Saygin, “Towards trajectory anonymization: a generalizationbased approach,” in Proc. of the SIGSPATIAL ACM GIS. 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ACM, 2009, pp. 72–83" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "PRIVACY PRESERVING LOCATION DATA PUBLISHING: A MACHINE LEARNING APPROACH" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i12.pp23-30", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "PRIVACY PRESERVING LOCATION DATA PUBLISHING: A MACHINE LEARNING APPROACH", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i12.pp23-30" } ], "agents": [ { "role": "author", "name": { "given": " D Nikhil Teja", "family": " D Nikhil Teja" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-12-12", "date_type": "PublicationDate", "online": "2023-12-12" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "12", "pages": { "first": "23", "last": "30" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/834", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/834/764", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/834/1658", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/Batch%2016%20CS.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "sensitive data is location trajectories. Spatiotemporal dataset is used in this framework, which include GPS trajectories for mobile users. The database includes k- anonymity for grouping the similar trajectories. The privacy metric for the publication of Spatiotemporal datasets is k- anonymity. The proposed algorithm is based on signature generation method, which is used to generate a key for the users in a digital manner. In signature generation method we use ECC algorithm for digital signature. We improved clustering approach and propose the 1,2,3B. Tech Student, Department of CSE (Cyber Security), Malla Reddy College of Engineering and Technology, Hyderabad, India. 4Assistant Professor, Department Of CSE (Data Science), Malla Reddy College Of Engineering and Technology, Hyderabad, India. DOI:https://doi.org/10.46243/jst.2023.v8.i12.pp23-30 15 D Nikhil Teja, Patan Abdulsalam Khan, A Sunny, R Shashi Rekha: PRIVACY PRESERVING LOCATION DATA PUBLISHING: A MACHINE LEARNING APPROACH k-means clustering. By using k-anonymity the similar trajectories were grouped and removed the dissimilar ones. Splitting techniques are used to protect the data privacy. In this paper, the proposed method is used to enhance the MLA framework to preserve the users privacy publication of Spatiotemporal datasets. The MLA framework has three algorithms: preprocessing, clustering, signature is used for the purpose of efficiency and security. The process of anonymization is to cluster. The ECC algorithm generate a private key and public key for the public users to see the personal details of a particular person. MLA algorithms are applied on real-world GPS datasets following different times and domains. Here the information loss is inversely proportional to the security level. Privacy preserving technique utilizes high quality datasets. Methods used in this paper are distribution of data, preprocessing the datasets, data mining algorithms, data hiding, signature generation, privacy preservation. The final results show the utility of the dataset and anonymization on MLA framework. LITERATURE SURVEY Data Privacy Through Optical K- Anonymization It proposes a practical method for determining an optimal anonymization of a given dataset. The optimal anonymization perturbs the input data as little as is necessary to achieve anonymity. Several different cost metrics have been proposed, through most aim in one way or another to minimize the amount of information loss results the generalization and suppression operations that are applied to produce the transformed dataset. The ability to compute optimal anonymizations let us more definitively investigate impacts of various coding techniques and problem variation on anonymization quality. It also allows to use better quantify the effectiveness of other nonoptimal methods. Winkler has proposed using simulated annealing to attack the problem but provides no evidence of its efficacy. The more theoretical side, Meyerson and Williams have recently proposed an approximation algorithm of optimal anonymization. Mondrian Multidimensional K- Anonymity Attacks can be reduced by using k- anonymity. The objective of k-anonymization technique is to protect the privacy of the every individual. The subject to this constrains, it is important that the released the data remain as “useful” as possible. This paper is a new multidimensional recoding model and a greedy algorithm for k-anonymization, an approach with several important advantages: the greedy algorithm has more efficient that proposed optimal kanonymization algorithms for single dimensional models. The greedy algorithm has the time complexity of O(nlogn), were the optimal algorithms are in worst cases. Higher quality results were produced while using greedy multidimensional algorithm than by using optimal single dimensional algorithm. Machanavajjhala Measured Anonymity by the l-diversity This paper proposed that uncertainty of linking QID with some particular sensitive values. Wang proposed to bond the confidence of inferring a particular sensitive value using one or more privacy templets specified the data provider. Wong proposed some generalization methods to simultaneously achieve k-anonymity and bond confidence. Xiao and Tao limited the breach probability, which is similar to the motion the confidence, and allowed a flexible threshold for each individual. K-anonymization for data owned by multiple parties for considered. T-closeness Privacy beyond K-anonymity and I-diversity While k-anonymity protect against the identity disclosure, it won‟t provide sufficient protection against attribute disclosure. The notion of I-diversity attempts to solve this problem by requiringthe equivalence class that has at least 1 well represented values for each sensitive attribute. We use the earth mover distance measure for ourcloseness requirement; thishas advantage of taking into", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-12-12", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/infocomwkshps47286.2019.9093762", "unstructured": "S. Shaham, M. Ding, B. Liu, Z. Lin, and J. Li, “Machine learning aided anonymization of spatiotemporal trajectory datasets,” arXiv preprint arXiv:1902.08934, 2019" }, { "key": "ref2", "unstructured": "A. Government, “New australian government data sharing and release legislation,” 2018" }, { "key": "ref3", "doi": "10.1109/titb.2012.2185850", "unstructured": "A. Tamersoy, G. Loukides, M. E. Nergiz, Y. Saygin, and B. Malin, “Anonymization of longitudinal electronic medical records,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 413–423, 2012" }, { "key": "ref4", "doi": "10.1145/3038912.3052620", "unstructured": "F. Xu, Z. Tu, Y. Li, P. Zhang, X. Fu, and D. Jin, “Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data,” in Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017, pp. 1241–1250" }, { "key": "ref5", "doi": "10.1016/j.knosys.2018.01.007", "unstructured": "Y. Dong and D. Pi, “Novel privacy-preserving algorithm based on frequent path for trajectory data publishing,” Knowledge-Based Systems, vol. 148, pp. 55–65, 2018" }, { "key": "ref6", "doi": "10.1109/infocom.2017.8056979", "unstructured": "M. Gramaglia, M. Fiore, A. Tarable, and A. Banchs, “Towards privacy- preserving publishing of spatiotemporal trajectory data,” arXiv preprint arXiv:1701.02243, 2017" }, { "key": "ref7", "doi": "10.1109/tkde.2017.2675420", "unstructured": "M. Terrovitis, G. Poulis, N. Mamoulis, and S. 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ACM, 2009, pp. 72–83" }, { "key": "ref11", "doi": "10.1109/access.2018.2822260", "unstructured": "B. Liu, W. Zhou, T. Zhu, L. Gao, and Y. Xiang, “Location privacy and its applications: A systematic study,” IEEE Access, vol. 6, pp. 17 606–17 624, 2018" }, { "key": "ref12", "doi": "10.1016/j.jbi.2016.11.001", "unstructured": "G. Poulis, G. Loukides, S. Skiadopoulos, and A. GkoulalasDivanis, “Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints,” Journal of biomedical informatics, vol. 65, pp. 76–96, 2017" }, { "key": "ref13", "doi": "10.1145/2351476.2351486", "unstructured": "T. Takahashi and S. Miyakawa, “Cmoa: Continuous moving object anonymization,” in Proceedings of the 16th International Database Engineering & Applications Sysmposium. ACM, 2012, pp. 81–90" }, { "key": "ref14", "doi": "10.1007/978-3-030-05755-8_44", "unstructured": "X. Zhou and M. Qiu, “A k-anonymous full domain generalization algorithm based on heap sort,” in International Conference on Smart Computing and Communication. 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Spatiotemporal dataset is used in this framework, which include GPS trajectories for mobile users. The database includes k- anonymity for grouping the similar trajectories. The privacy metric for the publication of Spatiotemporal datasets is k- anonymity. The proposed algorithm is based on signature generation method, which is used to generate a key for the users in a digital manner. In signature generation method we use ECC algorithm for digital signature. We improved clustering approach and propose the 1,2,3B. Tech Student, Department of CSE (Cyber Security), Malla Reddy College of Engineering and Technology, Hyderabad, India. 4Assistant Professor, Department Of CSE (Data Science), Malla Reddy College Of Engineering and Technology, Hyderabad, India. DOI:https://doi.org/10.46243/jst.2023.v8.i12.pp23-30 15 D Nikhil Teja, Patan Abdulsalam Khan, A Sunny, R Shashi Rekha: PRIVACY PRESERVING LOCATION DATA PUBLISHING: A MACHINE LEARNING APPROACH k-means clustering. By using k-anonymity the similar trajectories were grouped and removed the dissimilar ones. Splitting techniques are used to protect the data privacy. In this paper, the proposed method is used to enhance the MLA framework to preserve the users privacy publication of Spatiotemporal datasets. The MLA framework has three algorithms: preprocessing, clustering, signature is used for the purpose of efficiency and security. The process of anonymization is to cluster. The ECC algorithm generate a private key and public key for the public users to see the personal details of a particular person. MLA algorithms are applied on real-world GPS datasets following different times and domains. Here the information loss is inversely proportional to the security level. Privacy preserving technique utilizes high quality datasets. Methods used in this paper are distribution of data, preprocessing the datasets, data mining algorithms, data hiding, signature generation, privacy preservation. The final results show the utility of the dataset and anonymization on MLA framework. LITERATURE SURVEY Data Privacy Through Optical K- Anonymization It proposes a practical method for determining an optimal anonymization of a given dataset. The optimal anonymization perturbs the input data as little as is necessary to achieve anonymity. Several different cost metrics have been proposed, through most aim in one way or another to minimize the amount of information loss results the generalization and suppression operations that are applied to produce the transformed dataset. The ability to compute optimal anonymizations let us more definitively investigate impacts of various coding techniques and problem variation on anonymization quality. It also allows to use better quantify the effectiveness of other nonoptimal methods. Winkler has proposed using simulated annealing to attack the problem but provides no evidence of its efficacy. The more theoretical side, Meyerson and Williams have recently proposed an approximation algorithm of optimal anonymization. Mondrian Multidimensional K- Anonymity Attacks can be reduced by using k- anonymity. The objective of k-anonymization technique is to protect the privacy of the every individual. The subject to this constrains, it is important that the released the data remain as “useful” as possible. This paper is a new multidimensional recoding model and a greedy algorithm for k-anonymization, an approach with several important advantages: the greedy algorithm has more efficient that proposed optimal kanonymization algorithms for single dimensional models. The greedy algorithm has the time complexity of O(nlogn), were the optimal algorithms are in worst cases. Higher quality results were produced while using greedy multidimensional algorithm than by using optimal single dimensional algorithm. Machanavajjhala Measured Anonymity by the l-diversity This paper proposed that uncertainty of linking QID with some particular sensitive values. Wang proposed to bond the confidence of inferring a particular sensitive value using one or more privacy templets specified the data provider. Wong proposed some generalization methods to simultaneously achieve k-anonymity and bond confidence. Xiao and Tao limited the breach probability, which is similar to the motion the confidence, and allowed a flexible threshold for each individual. K-anonymization for data owned by multiple parties for considered. T-closeness Privacy beyond K-anonymity and I-diversity While k-anonymity protect against the identity disclosure, it won‟t provide sufficient protection against attribute disclosure. The notion of I-diversity attempts to solve this problem by requiringthe equivalence class that has at least 1 well represented values for each sensitive attribute. We use the earth mover distance measure for ourcloseness requirement; thishas advantage of taking into", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-12-12", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/infocomwkshps47286.2019.9093762", "unstructured": "S. Shaham, M. Ding, B. Liu, Z. Lin, and J. Li, “Machine learning aided anonymization of spatiotemporal trajectory datasets,” arXiv preprint arXiv:1902.08934, 2019" }, { "key": "ref2", "unstructured": "A. Government, “New australian government data sharing and release legislation,” 2018" }, { "key": "ref3", "doi": "10.1109/titb.2012.2185850", "unstructured": "A. Tamersoy, G. Loukides, M. E. Nergiz, Y. Saygin, and B. Malin, “Anonymization of longitudinal electronic medical records,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 413–423, 2012" }, { "key": "ref4", "doi": "10.1145/3038912.3052620", "unstructured": "F. Xu, Z. 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