A Privacy Preserving Scheme for Big data Publishing in the Cloud using k-Anonymization and Hybridized Optimization Algorithm

Madan, Suman; Goswami, Puneet

doi:10.1109/iccsdet.2018.8821140

Cited by 17 publications

(7 citation statements)

References 15 publications

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“…Regulation here can curb the adverse effects of these negative externalities arising from trading and significantly contribute to welfare efficient and complete markets (where supply equals demand) [25] [26]. Examples of practical ways to implement regulations suggested in existing literature include legislative property rights on consumer personal data shared between the supply and demand side [14], technical metrics (e.g., DP) being adopted by demand side data intermediaries (e.g., ad-networks) to check on the degree of IP breach [6], and frameworks such as those developed in [27,28,29,30] to improve security and privacy for BigData systems (e.g., HDFS). Specifically, in relation to the data intermediary settings such as in Figure 1, De Corni'ere and Nijs [31] For more details on the interplay between differential privacy and mechanism design, [7] gives a comprehensive survey.…”

Section: Related Literaturementioning

confidence: 99%

Preference-Based Privacy Markets

et al. 2020

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In the modern era of the mobile apps (the era of surveillance capitalism-as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising-a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in longterm benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are 'compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.

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Section: Related Literaturementioning

confidence: 99%

Preference-Based Privacy Markets

et al. 2020

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“…Therefore, a new dragon PSO algorithm is suggested to establish a k-anonymization criteria to derive the fitness function that represents the privacy and utility levels of cloud migration services. The experiments using an Adult database verify the efficiency of the privacy-preserved model against information loss risks [157]. Moreover, the work in [158] presents a data privacy-preserved PSO algorithm for cloud computing.…”

Section: B Data Privacymentioning

confidence: 73%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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“…Madan and Goswami 37 have proposed an anonymization-based model for the protection and privacy preservation in the cloud. This method uses k-anonymity criteria, which uses the presented Dragonfly-based Particle Swarm Optimization (Dragonfly-PSO) algorithm to copy K records in the database.…”

Section: Related Workmentioning

confidence: 99%

“…To preserving anonymity, rules to restrict the sharing of patient-specific medical data have particular importance. 37,38 This research helps maintain privacy in the healthcare environment by providing a combined method for developing and improving the anonymization model. Section 3.1 defines the recommended system's architecture to the healthcare cloud.…”

Section: Proposed Workmentioning

confidence: 99%

A clustering‐based anonymization approach for privacy‐preserving in the healthcare cloud

Abbasi

Mohammadi

2021

Concurrency and Computation

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Today, modern healthcare systems rely on advanced computational technologies, including cloud-based systems, to gather and examine personal health data on a large scale. The use of advanced cloud services technologies, such as software as a service, application as a service, is challenging for end-users of cloud systems to protect sensitive data in their health applications. According to the importance of publishing data in the cloud, the information should be recorded and handled somehow so that any individuals' identity remains hidden. Therefore, one of the critical privacy challenges is protecting the quality of published data and privacy-preserving on the healthcare cloud simultaneously. The K-anonymity technology is one of the prevalent methods used for privacy-preserving. In this article, we suggest a novel approach based on the clustering process using the K-means++ method to achieve an optimal k-anonymity algorithm. Also, we use the normal distribution function to delete data that is less frequent, which can be improved the quality of anonymized data. Extensive experiments show the proposed method has been able to reduce information loss 1.5 times and execution time 3.5 times compared to AKA and GCCG algorithms. Also, it is highly scalable than others.

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A Privacy Preserving Scheme for Big data Publishing in the Cloud using k-Anonymization and Hybridized Optimization Algorithm

Cited by 17 publications

References 15 publications

Preference-Based Privacy Markets

Preference-Based Privacy Markets

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

A clustering‐based anonymization approach for privacy‐preserving in the healthcare cloud

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