Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks

Langari, Rohulla Kosari; Sardar, Soheila; Mousavi, Seyed Abdollah Amin; Radfar, Reza

doi:10.1016/j.eswa.2019.112968

Cited by 60 publications

(26 citation statements)

References 14 publications

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“…It is proved that this algorithm can prevent the sensitive variance attack and categorical similarity attack. Rohulla kosari langari et al [27] propose a privacy preserving method KFCFA in social networks, which uses k-member fuzzy clustering for clustering and optimizes the clustering and anonymization process with the Firefly algorithm. The KFCFA method protects privacy on the data level and graph level, and effectively reduces information loss.…”

Section: Anonymous Methodsmentioning

confidence: 99%

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A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

2021

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With the rapid development of the Internet of Things (IoT), wearable devices have become ubiquitous and interconnected in daily lives. Because wearable devices collect, transmit, and monitor humans’ physiological signals, data privacy should be a concern, as well as fully protected, throughout the whole process. However, the existing privacy protection methods are insufficient. In this paper, we propose a practical privacy-preserving mechanism for physiological signals collected by intelligent wearable devices. In the data acquisition and transmission stage, we employed existing asymmetry encryption-based methods. In the data publishing stage, we proposed a new model based on the combination and optimization of k-anonymity and differential privacy. An entropy-based personalized k-anonymity algorithm is proposed to improve the performance on processing the static and long-term data. Moreover, we use the symmetry of differential privacy and propose the temporal differential privacy mechanism for real-time data to suppress the privacy leakage while updating data. It is proved theoretically that the combination of the two algorithms is reasonable. Finally, we use smart bracelets as an example to verify the performance of our mechanism. The experiment results show that personalized k-anonymity improves up to 6.25% in terms of security index compared with traditional k-anonymity, and the grouping results are more centralized. Moreover, temporal differential privacy effectively reduces the amount of information exposed, which protects the privacy of IoT-based users.

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Section: Anonymous Methodsmentioning

confidence: 99%

“…where D is the average of total data in D. IL is the ratio of SSE and SST, which is expressed in Equation (27).…”

Section: Privacy Preserving On Static Datamentioning

confidence: 99%

A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

2021

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“…Privacy-protected data publishing based on clustering has attracted more attentions. Langari et al 26 proposed a combined anonymity algorithm based on k -means clustering to minimize information loss and protect user privacy in social networks. Dou et al 27 proposed a privacy protection data aggregation algorithm based on primitive clustering, which can effectively reduce data traffic and improve data privacy.…”

Section: Related Workmentioning

confidence: 99%

MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things

Zhang

Tongtong

Sun

et al. 2021

International Journal of Distributed Sensor Networks

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The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user’s privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k-medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k-medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k-medoids clustering, multiple partition differential privacy k-medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user’s privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering.

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“…Work described in [11] proposes a combined anonymizing algorithm based on K-member Fuzzy Clustering and Firefly Algorithm (KFCFA) to protect the anonymized database against identity disclosure, attribute disclosure, link disclosure, and similarity attacks, and significantly minimize the information loss.…”

Section: Related Workmentioning

confidence: 99%

Soft Computing for Scalability in Context Aware Location based Services

Jagwani

Kaushik

2020

IJACSA

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Ubiquitous computing blended with context awareness gives user the facility of "anywhere anytime" computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user's privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity.The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error.

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Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks

Cited by 60 publications

References 14 publications

A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things

Soft Computing for Scalability in Context Aware Location based Services

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