The cyclical and continuous working characteristics ofInternet of Things(IoT) devices make a large amount of the same or similar data, which can significantly consume storage space. To solve this problem, various secure data deduplication schemes have been proposed. However, existing deduplication schemes only perform deduplication based on data similarity, ignoring the internal connection among devices, making the existing schemes not directly applicable to parallel and distributed scenarios like IoT. Furthermore, since secure data deduplication leads to multiple users sharing same encryption key, which may lead to security issues. To this end, we propose a device relationship-based IoT data deduplication scheme that fully considers the IoT data characteristics and devices internal connections. Specifically, we propose a device relationship prediction approach, which can obtain device collaborative relationships by clustering the topology of their communication graph, and classifies the data types based on device relationships to achieve data deduplication with different security levels. Then, we design a similarity-preserving encryption algorithm, so that the security level of encryption key is determined by the data type, ensuring the security of the deduplicated data. In addition, two different data deduplication methods, identical deduplication and similar deduplication, have been designed to meet the privacy requirement of different data types, improving the efficiency of deduplication while ensuring data privacy as much as possible. We evaluate the performance of our scheme using five real datasets, and the results show that our scheme has favorable results in terms of both deduplication performance and computational cost.

With the rise of the Internet of Things (IoT), IoT data analytics has gradually stepping into the spotlight of data mining. Existing research has primarily focused on enhancing the precision of IoT data mining, while the privacy protection aspects have not been fulfilled so far. The deployment of IoT data mining is contingent on the protection of data privacy and its economic worth to all parties. However, most existing IoT data mining research disregards economic benefits and lacks incentives, limiting its applicability. To address this issue, we provide a system for differential privacy-based IoT privacy-preserving data mining (IoT-PPDM) with dynamic incentive mechanism, and a formal economic model for IoT data mining is constructed. We utilized noncooperative game theory to simulate the multilateral interaction process in IoT data mining. To encourage participation from all parties, a dynamic incentive mechanism is designed to establish a balance between privacy protection and data mining requirements. In addition, we discuss the utility of all participants and theoretically validate the feasibility of IoT-PPDM. The experimental results show that IoT-PPDM with dynamic incentive mechanism can increase the benefits for all participants while avoiding irrational behavior of all parties.

With the development of the Industrial Internet of Things (IIoT), the scale of IIoT data is rapidly increasing, bringing significant challenges to existing data management systems. To tackle this issue, we propose a similarity-based secure deduplication for IIoT cloud management system, which can effectively balance the security and availability of IIoT data and minimize the storage cost. Concretely, we propose a similarity-based secure deduplication algorithm for IIoT (IIoT-SBSD) by designing a similarity-preserving tag (IIoT-Simhash). This algorithm can perform similarity deduplication over ciphertexts, thus reducing storage space while ensuring data security. Besides, we construct a parallelizable edge-based deduplication framework in which similarity comparison and deduplication operations are performed directly by edge nodes, significantly alleviating the transmission pressure. Additionally, we propose similarity-based proofs of ownership, S-PoWs, to mitigate the impact of data deduplication on the user's access to the IIoT data. Experimental results show that our method significantly reduces storage space and transmission bandwidth without increasing the computation burden.

With the rapid development of Internet of Things (IoT), big data analytics (BDA) has gradually stepped into the spotlight of IoT research. However, existing research mostly focused on improving the efficiency of data mining, the wishes and profits of the participants in BDA are largely ignored. In real systems, it is always supposed that the IoT data owners accept the big data analytics in IoT (BDA-IoT), the theoretical modeling of BDA-IoT is needed to be constructed urgently. In this paper, aiming to provide theoretical modeling of the practical application of BDA-IoT, we prove the feasibility of participants voluntarily participating in BDA-IoT for the first time. Subsequently, a non-cooperative game theory model with incentive and payment mechanisms is constructed, and the multi-parties interaction process in BDA-IoT is simulated. The overall benefits of all participants are then discussed, the rationality and feasibility of our study are proved. Simulation results show the feasibility of our model in improving the benefits of all stakeholders in a non-cooperative game, and the best choice for all participants is to accept BDA-IoT.