Privacy Preserving Approach of Published Social Networks Data with Vertex and Edge Modification Algorithm

Khazali, Mohammad Javad; Sargolzaei, Ehsan; Keikha, Fateme

doi:10.17485/ijst/2016/v9i12/81982

Cited by 3 publications

(2 citation statements)

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“…K-anonymous makes at least k records have the same quasi identifier by generalization or clustering. If k records in the data set have the same quasi-identifier, the attacker has only 1/k probability to guess out the correct result [32]. The method is easy to implement, and the leakage risk is measurable, so it is widely used [26].…”

Section: Introductionmentioning

confidence: 99%

A New Method of Privacy Protection: Random k-Anonymous

Song

Tian

et al. 2019

IEEE Access

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A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved. Since the process of finding anonymous equivalence is very time consuming, a two-step clustering method is used to divide the original data set into equivalence classes. First, the original data set is divided into several different sub-datasets, and then the equivalence classes are formed in the sub-datasets, thus greatly reducing the computational cost of finding anonymous equivalence classes. The experiments are conducted on three different data sets, and the results show that the proposed method is more efficient and the information loss of anonymous dataset is much smaller.

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Section: Introductionmentioning

confidence: 99%

A New Method of Privacy Protection: Random k-Anonymous

Song

Tian

et al. 2019

IEEE Access

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“…In [6] described a method to preserve the privacy of the published data by modifying the graph by adding a small number of edges. This method provided the quantitative value of lost information due to the generalization of the labels.…”

Section: Introductionmentioning

confidence: 99%

An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

Umapathy

Khare

2018

IJEECS

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<span lang="EN-US">Online Social Networks (OSNs) is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no content-based preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previously defined web content mining and security techniques. To overcome the issues, Level-level Security Optimization & Content Visualization Algorithm is proposed to avoid the privacy issues during content sharing and data visualization. It adopts level by level privacy based on user requirement in the social network. It evaluates the privacy compatibility in the online social network environment to avoid security complexities. The mechanism divided into three parts namely like online social network platform creation, social network privacy, social network within organizational privacy and network controlling and authentication. Based on the experimental evaluation, a proposed method improves the privacy retrieval accuracy (PRA) 9.13% and reduces content retrieval time (CRT) 7 milliseconds and information loss (IL) 5.33%.</span>

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Social Networks Privacy Preservation: A Novel Framework

Singh

2022

Cybernetics and Systems

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Privacy Preserving Approach of Published Social Networks Data with Vertex and Edge Modification Algorithm

Cited by 3 publications

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A New Method of Privacy Protection: Random k-Anonymous

A New Method of Privacy Protection: Random k-Anonymous

An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

Social Networks Privacy Preservation: A Novel Framework

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