Abstract
Users can obtain intelligent services by sharing information in social networks. Big data technologies can discover underlying benefits from this information. However, stringent security concern is raised at the same time. The public data can be utilized by adversaries, which will bring dire consequences. In this paper, the influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. At first, in order to estimate the risk level for each user, a Bayesian-based individual privacy risk evaluation model is proposed to rank the individual risk levels. Secondly, as the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Finally, based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. In addition, the comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently.
Highlights
Big data sharing through social media has meaningfully grown in the current era of social network [1, 2]. e social media has assumed great importance through WeChat, Facebook, social network sites, and Twitter. e issue of how information spreads through the social network has drawn more and more attention [3, 4]. e publicly available data can be utilized for market analysis, social research, and personalized service formulation [5]
Influence maximization problem in privacy protection environment is to find a subset of secure and reliable users that can make the spread of influence maximization and privacy disclosure minimization
It is a critical problem of finding the main factors of privacy risk and estimating the risk level of these factors [17]. ere are many kinds of attributes for the shared big data, which can be classified into three categories: quasi-attributes, direct attributes, and sensitive attributes [18]
Summary
Received 13 November 2021; Revised 17 December 2021; Accepted 9 January 2022; Published 1 February 2022. Big data technologies can discover underlying benefits from this information. E public data can be utilized by adversaries, which will bring dire consequences. The influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. As the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. The comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently
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