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Title	Techniques For Privacy Preserving Data Publication In The Cloud For Smart City Applications
ID_Doc	54531
Authors	Aldeen Y.A.A.S.; Salleh M.
Year	2018
Published	Smart Cities Cybersecurity and Privacy
DOI	http://dx.doi.org/10.1016/B978-0-12-815032-0.00010-X
Abstract	Numerous modern cities aim to integrate information technology into each appearance of city life to create so-called smart cities. A large number of application areas and technologies are utilized in smart cities to achieve complex interactions between citizens, city departments, and third parties. This great complexity is one reason why privacy protection only seldom comes into the picture. A violation of privacy can result in discrimination and social sorting. Moreover, recent advancements in information and communication technologies (ICT) have demanded much of cloud services in respect to sharing users’ private data. Data from various organizations are the vital information source for analysis and research. Generally, this sensitive or private data involves information related to medical records, census data, voter registration, social networks, and customer services. A primary concern of cloud service providers in data publishing is to hide the sensitive information of individuals. One of the cloud services that fulfils these confidentiality concerns is privacy preserving data mining (PPDM). The PPDM service in cloud computing (CC) enables data publishing with minimized distortion and absolute privacy. In this method, datasets are anonymized via generalization to meet privacy requirements. However, the well-known privacy-preserving data mining technique called K-anonymity suffers from several limitations. To surmount those shortcomings, we propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing in the cloud for smart cities. The advantages of K-anonymity, L-diversity, and (α, k)-anonymity methods for efficient information utilization and privacy protection are emphasized. Experimental results have revealed the superior performance of these techniques. © 2019 Elsevier Inc. All rights reserved.
Author Keywords	Anonymization; Cloud computing; Privacy; Smart city

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