IEEE Systems, Man and Cybernetics Magazine - October 2022 - 45

although various algorithms explore the best solution to
maximize influence from different angles, they ignore the
privacy protection problem in communication [5], [6], [7],
[8], [9]. User privacy is the most common information carrier
for social networks, and influence dissemination
schemes lacking privacy protection measures are challenging
to directly apply to the actual environment. Therefore,
in this article, aiming at the reasons why the existing information
dissemination models cannot well model the leakage
of user privacy and the problem that related research
is challenging to balance the needs of user privacy protection
and information dissemination, this work researches
social network information dissemination methods that
support privacy protection [10], [11], [12]. It helps to maximize
the dissemination of information influence while protecting
user privacy. The main applications of this method
are as follows (see Figure 1):
1) For individual users: Only the blocked users are specified
when publishing information. Therefore, the algorithm
can automatically give the push objects of the data
so that the information will not leak to the blocked users.
2) For merchants: By setting non-target users as block
lists, algorithms can find out which users in the social
network are more valuable for advertising and push
them accurately.
that people trusted by users only access user information,
it is necessary to implement information access control
between user nodes for privacy protection. The authors in
[1] designed a centralized social network model that supports
high availability and real-time content dissemination
and decides users' privacy by learning their social behaviors.
The authors in [2] proposed online social network
architecture Lotus Net based on a distributed hash table
which controls private resources through a flexible and
fine-grained autonomous access control unit, making it
easier for users to adjust their privacy settings. In practice,
however, neither centralized nor distributed solutions can
protect pervasive social network communications as
expected. To this end, a kind of access control scheme
based on 2D trust degree is proposed [3], [4], which calculates
the 2D trust degree between nodes by using information
node attributes and implements access control to user
privacy according to 2D trust degree. To summarize, in the
existing information dissemination prediction research,
researchers have not considered the possibility of using
paraphrasing as an information dissemination method,
which will cause the omission of the dissemination path
when tracing the source of disseminated information, as
that in information dissemination modeling. Leaks of private
information cannot be captured when a paraphrase
occurs. In the research of influence maximization,
Methods of Constructing Communication
Networks to Support Retelling Behaviors
For new information to be released by users, to maximize
the influence of information dissemination while satisfying
the preset privacy protection constraints, it is necessary
to predict the information dissemination process
based on the social network dissemination model. The
social network propagation model relies on the probability
distribution of user behavior, so it is necessary to build a
propagation network of published information to train a
user behavior classifier.
Method Overview
Problem Definition
The difficulty of building a communication network is that
there are only two types of user behaviors obtained from
1
2
3
4
5
6
Figure 1. The process of information communication
based on the model of social network communication.
October 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 45
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IEEE Systems, Man and Cybernetics Magazine - October 2022

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