Lightweight Sybil Attack Detection in IoT Based on Bloom Filter and Physical Unclonable Function

2021 ◽  
pp. 102541
Author(s):  
Cong Pu ◽  
Kim-Kwang Raymond Choo

Healthcare media provides a capable model in order to fascinate consumer to discuss their health information and access health protection facilities from connected caretakers. Since the traditional health care service is time consuming process, we are switching to online health care practice, which can reduce the gap between care takers and patients. Due to the frankness of the social network the trust between the patients and care takers is a challenging issue and there is a chance of revealing personal information of the patients. Here, we intended to propose a reliable health protection service to facilitate user in social media networks, we deploy Bloom Filter technique for suitable personalized caretakers, in order to assure trustworthy rankings and critiques for caretakers, we include Sybil attack detection technique to identify users’ fake rankings and critiques using various false name. It incorporates generalization and suppression techniques to protect individual’s private data. For this purpose, k-anonymity Technique is implemented to anonymize the data.


Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 198
Author(s):  
Junhyeok Yun ◽  
Mihui Kim

Mobile crowdsensing is a data collection system using widespread mobile devices with various sensors. The data processor cannot manage all mobile devices participating in mobile crowdsensing. A malicious user can conduct a Sybil attack (e.g., achieve a significant influence through extortion or the generation of fake IDs) to receive an incentive or destroy a system. A mobile crowdsensing system should, thus, be able to detect and block a Sybil attack. Existing Sybil attack detection mechanisms for wireless sensor networks cannot apply directly to mobile crowdsensing owing to the privacy issues of the participants and detection overhead. In this paper, we propose an effective privacy-preserving Sybil attack detection mechanism that distributes observer role to the users. To demonstrate the performance of our mechanism, we implement a Wi-Fi-connection-based Sybil attack detection model and show its feasibility by evaluating the detection performance.


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