Conceptualizing Distrust Model with Balance Theory and Multi-Faceted Model for Mitigating False Reviews in Location-Based Services (LBS)

Abstract

Location-based services (LBS) use real-time geo-data from a smartphone to provide information, entertainment or surveillance information. However, the reputations of LBS application have raised some privacy and security issues such as location tracked by third parties and creation of fake reviews and events through Sybil attack. Fake events on LBS such as congestion, accidents or police activity affect routes users and fake reviews caused nuisances and decreases trust towards this technology. The current trust model in LBS is single faceted and not personalized. The concept of both trust and distrust are essential criteria of any trust management model to measure the reliability of LBS applications. This paper explores the relationship between trust models and the distrust concept in LBS. By deriving a representation of the multi-faceted model and balance theory conceptualized in a MiniLBS prototype, trust in this technology is quantified. By adopting matrix factorization and probability algorithms on the survey results, the relationship between distrust and trust is further examined and tested. The result obtained from the experiment was nearly zero, the smallest one was 3.0253 × 10−95, and the largest value was only 4.967 × 10−43. The results show that distrust is not a negation of trust. Another crucial finding suggests that balance theory within distrust in the LBS trust model can enhance the trust management model in LBS and indirectly cater issues rise from fake event problem.