Interaction-Based Reputation Model in Online Social Networks
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Interaction-based Reputation Model in Online Social Networks Izzat Alsmadi1, Dianxiang Xu2 and Jin-Hee Cho3 1Department of Computer Science, University of New Haven, West Haven, CT, U.S.A. 2Department of Computer Science, Boise State University, Boise, ID, U.S.A. 3US Army Research Laboratory, Adelphi, MD, U.S.A. Keywords: Online Social Networks, Reputation, User Attributes, Privacy, Information Credibility. Abstract: Due to the proliferation of using various online social media, individual users put their privacy at risk by posting and exchanging enormous amounts of messages or activities with other users. This causes a seri- ous concern about leaking private information out to malevolent entities without users’ consent. This work proposes a reputation model in order to achieve efficient and effective privacy preservation in which a user’s reputation score can be used to set the level of privacy and accordingly to determine the level of visibility for all messages or activities posted by the users. We derive a user’s reputation based on both individual and relational characteristics in online social network environments. We demonstrate how the proposed reputation model can be used for automatic privacy assessment and accordingly visibility setting for messages / activities created by a user. 1 INTRODUCTION likes, tags, posts, comments) to calculate the degree of friendship between two entities (Jones et al., 2013). As the use of online social network (OSN) appli- An individual user’s reputation can be determined cations becomes more and more popular than ever, based on a variety of aspects of the user’s attributes. many people share their daily lives by posting stories, One of the important attributes is whether the user comments, videos, and/or pictures, which may ex- feeds quality information such as correct, accurate, pose their private / personal information (Mansfield- or credible information. While users are free to Devine, 2008). In such contexts, privacy became a post their opinions, OSNs should promote propa- critical issue because private information can be ex- gation of credible information to maintain friendly, posed to the public (e.g., by search engines) without healthy OSN environments. To this end, we pro- the user’s consent. pose our reputation model in which reputation is com- This work proposes a reputation model that eval- puted based on a user’s personal attributes embrac- uates an individual’s reputation based on his/her in- ing his/her background and affiliations and relational teractions with peers such as friends in OSNs. Rep- attributes including interactions with others and the utation is defined as an opinion about an entity, ei- reputation levels of the user’s friends. ther something or someone including a person, orga- An example scenario can be as follows. User i nization, service, etc., based on third parties’ opinions continuously posts highly helpful, relevant informa- (e.g., recommendations). In particular, many OSN tion in OSNs. When user j continuously interacts sites have different mechanisms to estimate reputa- with those posts fed by users i and j will be listed tion. For example, Facebook uses the concept of a as one of top contributors for those activities. User friend with three different levels such as close friend, j can list user i as one of his/her top peers. In terms friends, and acquaintances in order to categorize the of user j’s reputation, user i, one of j’s top peers, is closeness-based friendship. This kind of friends can a key contributor to improve user j’s reputation. Ac- be designated by an individual user and does not rep- cordingly, user i will be encouraged to post such types resent the degree of friendship based on the amount of activities that may attract more interactions with of interactions between two individual users. Similar other peers, leading to increasing her/his reputation to web ranking algorithms, some algorithms of scor- indirectly. ing friendship use the number of interactions (e.g., Many OSN applications are equipped with their 265 Alsmadi, I., Xu, D. and Cho, J-H. Interaction-based Reputation Model in Online Social Networks. DOI: 10.5220/0005668302650272 In Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP 2016), pages 265-272 ISBN: 978-989-758-167-0 Copyright c 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved ICISSP 2016 - 2nd International Conference on Information Systems Security and Privacy own algorithms to estimate peer relationships. For in- user. The reputation-based privacy setting can be stance, Facebook calculates “close friends” or ranks an adaptive support mechanism for a user to set each friend based on interactions including profile his/her privacy setting more effectively and effi- view, group interactions, the number of likes and/or ciently. recency of interactions. In this work, we aim to pro- The rest of this work is organized as follows. Section pose a generic model of reputation which can rep- 2 discusses how an entity’s reputation is derived by resent comprehensive aspects of an individual’s trust describing each component of reputation. Section 3 based on collected information for statistical calcu- describes how the proposed reputation model is ap- lation / analysis but without revealing any private plied to privacy assessment in OSNs. Section 4 gives content of an individual such as activity content, or an overview of related work in terms of existing mod- friends’ identities without the user’s consent. There- els of trust, reputation, and privacy in OSNs. Section fore, our proposed reputation model is to assess an in- 5 concludes this work and suggests future work direc- dividual’s reputation based on his/her individual and tions. relational attributes while preserving the individual’s privacy. This paper has the following contributions: We propose a reputation model in which reputa- 2 REPUTATION MODEL • tion score is derived from multiple aspects of a The focus of the proposed reputation system is on user, particularly, in terms of individual attributes how to derive reputation score for a user, either an in- and relational attributes. This way of estimat- dividual user or an organization, who has an account ing reputation is to ensure the quality of an in- in social network media. In this work, we consider a formation provider by assessing a user’s personal graph G where an entity is a vertex, v and the social attribute (e.g., work, education, interest groups) i relationship between two entities, i and j, is an edge, while assuring information credibility based on e , as many studies assume. If two entities are con- the popularity of activities created by the user. In i, j nected with an edge, this means the two entities are addition, by assessing the quality of peers exhibit- friends to each other. But note that the degree of trust ing active interactions through the user’s activi- a different entity has towards a same third-party entity ties, the reputation score of an entity can repre- is different, implying the trust relationship is subjec- sent multidimensional aspects of trustworthiness tive and asymmetric (Cho et al., 2011). in each entity. We estimate an individual user’s reputation score Although an entity’s reputation value is de- based on the following three components: • rived from multidimensional aspects of his/her at- Personal attributes including occupation, educa- tributes, the proposed reputation model aims to • preserve the user’s privacy by not exposing any tion level, affiliated groups, favorites, interest, or information to any peers without the user’s con- political leaning which may represent an entity’s sent. In particular, since reputation is estimated social identity; based on the amount of interactions but the con- Personal activities representing the quality and tent of activities / messages, an entity’s privacy • quantity of interactions with other friends through is well preserved unless the entity permits special posts, likes, tweets, creation of interest-driven actions to manually set the privacy level (e.g., spe- groups; cial permission for a spouse to see all the activities Characteristics associated with an individual’s / messages although he / she is not active on on- • friends indicating the amount of interactions with line interactions). friends and the friends’ reputation levels; The proposed reputation model is adaptive to re- • The first component is called individual attributes flect the dynamic evolution of an entity’s reputa- while the other two components are called relational tion. As an entity’s interactions or activities are attributes. Figure 1 describes what characteristics of updated, the entity’s reputation is updated as well an individual user are considered to derive a reputa- and accordingly it will be reflected in updating tion score as discussed above. To be specific, top L privacy setting for each peer on visibility of ac- personal attributes, top M personal activities, and top tivities / messages the user posts. In addition, our K peers are considered to derive the user’s overall rep- reputation model allows human intervention (e.g., utation. Individual user i’s reputation score, Ri, can be a user can manually modify privacy setting for a given by: particular peer) when the automated system set- ting of privacy is not agreed with the need of a R = α P + β P + µ R (1) i × i,L × i,M × i,K 266 Interaction-based Reputation Model in Online Social Networks have its individual online social network page. All the entities may have their gateways to the different OSNs in addition to the main entity portal. For some entities, their social network pages are visited as of- ten as their main portals by having a great number of members, friends, or subscribers. Estimated reputa- tion scores of those entities can be used for several possible use cases.