M2M-REP: Reputation system for machines in the internet of things.
|Authors||Azad, Muhammad Ajmal, Bag, Samiran, Hao, Feng and Salah, Khaled|
In the age of IoT (Internet of Things), Machine-to-Machine (M2M) communication has gained significant popularity over the last few years. M2M communication systems may have a large number of autonomous connected devices that provide services without human involvement. Interacting with compromised, infected and malicious machines can bring damaging consequences in the form of network outage, machine failure, data integrity, and financial loss. Hence, users first need to evaluate the trustworthiness of machines prior to interacting with them. This can be realized by using a reputation system, which evaluates the trustworthiness of machines by utilizing the feedback collected from the users of the machines. The design of a reliable reputation system for the distributed M2M communication network should preserve user privacy and have low computation and communication overheads. To address these challenges, we propose an M2M-REP System (Machine to Machine REPutation), a privacy-preserving reputation system for evaluating the trustworthiness of autonomous machines in the M2M network. The system computes global reputation scores of machines while maintaining privacy of the individual participant score by using secure multi-party computation techniques. The M2M-REP system ensures correctness, security and privacy properties under the malicious adversarial model, and allows public verifiability without relying on a centralized trusted system. We implement a prototype of our system and evaluate the system performance in terms of the computation and bandwidth overhead.
|Keywords||Machine to machine communications; Edge Computing; Internet of Things; Trust Computation|
|Journal||Computers & Security|
|Journal citation||79, pp. 1-16|
|Digital Object Identifier (DOI)||https://doi.org/10.1016/j.cose.2018.07.014|
|Web address (URL)||http://hdl.handle.net/10545/623827|
|Publication dates||14 Aug 2018|
|Publication process dates|
|Deposited||11 Jun 2019, 10:45|
|Accepted||06 Jul 2018|
|Contributors||Newcastle University and Khalifa University|
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