Interface Implementation for Quantifying Information Spread on Social Networks

Kumbhojkar, Prajakta; Jain, Masumi; Rajalakshmi, E.; Rawal, Shyamsalonee; Thombre, Sneha

doi:10.1109/mite.2018.8747013

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Network data are digitally collected and the aggregated data are often published, shared or sold to third parties (such as analytics companies, marketing companies or commercial data brokers) for further analysis. Some applications of network data include analyzing the formation of communities [1], marketing and advertising [2], [3], opinion modeling [4], network information spread [5], criminal analysis [6], [7], shortest paths analysis [8]- [11] and spanning trees [12], [13]. Privacy in the applications of Adhoc social networks [14] and non Ad-hoc social networks [15] are also gaining the public concerns.…”

Section: Introductionmentioning

confidence: 99%

Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Chong

Malip

2021

IEEE Access

View full text Add to dashboard Cite

Privacy in social networks has been a vast active area of research due to the enormous increase in privacy concerns with social networking services. Social networks contain sensitive information of individuals, which could be leaked due to insecure data sharing. To enable a secure social network data publication, several privacy schemes were proposed and built upon the anonymity of users. In this paper, we incorporate unlinkability in the context of weighted network data publication, which has not been addressed in prior work. Two key privacy models are defined, namely edge weight unlinkability and node unlinkability to obviate the linking of auxiliary information to a targeted individual with high probability. Two new schemes satisfying these unlinkability notions, namely MinSwap and δ-MinSwapX are proposed to address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. The edge weight is modified based on minimum value change in order to preserve the usefulness and properties of the edge weight data. In addition, edge randomization is performed to minimally modify the structural information of a user. Experimental results on real data sets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.

show abstract