Information Integrity In Participatory Crowd-Sensing Via Robust Trust Models


In this chapter, we propose a recipe for event truthfulness scoring and user reputation scoring framework that is immune to the cold start problem in participatory mobile crowd-sensing applications, while being robust to attacks that target operational and AI-based weaknesses in mobile crowd-sensing and existing trust models. Our method does not need to depend on the knowledge of ground truth nor the existence of a prior user reputation, both of which are impractical assumptions during the cold start phase. Specifically, we first show subtle variations of dishonest intent in terms of fake event reporting attacks as an operational vulnerability. Additionally, we show threats that weaponize design provisions that help assess event truthfulness, in the form of feedback weaponizing attacks as an AI-based vulnerability. Furthermore, we show how existing methods of trust and reputation should be modified to jointly mitigate the effects of both fake event reporting and feedback weaponizing attacks, during the cold start, by using a vehicular crowd-sensing application as a proof-of-concept. Our design modifications are inspired by cognitive psychology, behavioral economics, and symbolic AI, and how they can be seamlessly embedded into known approaches for trust and reputation scoring.


Computer Science


National Science Foundation, Grant OAC-2017289

Keywords and Phrases

Crowd-sensing; Information integrity; Robust trust models

International Standard Serial Number (ISSN)

2366-1445; 2366-1186

Document Type

Article - Journal

Document Version


File Type





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Publication Date

01 Jan 2023