Recently, many decentralized IoT systems incorporating IPFS and blockchain have been widely used to share private data and overcome issues in a centralized IoT system such as a single point of failure and loss of data sovereignty. However, the constrained computing power of IoT devices can result in a performance overhead during encryption processes, which involves computationally intensive tasks. In addition, the blockchain involved in the access control mechanism can raise another performance issue and potentially expose sensitive information due to its transparent nature. To address these issues, in this paper, we propose an efficient IoT framework (EnC-IoT) to enhance performance and data protection (i.e., security and privacy) in a decentralized IoT environment. We first introduce an efficient data encryption scheme that shuffles the data order to enhance the security level. Then, it partitions the data into multiple regions and encrypts/decrypts each region in parallel to accelerate the cryptography process. Second, we provide an efficient access control scheme which adopts a tree-based authentication to perform the access control quickly. Moreover, it employs a proxy re-encryption method which allows the decryption of sensitive information without exposing the owner’s private key to improve both security and privacy levels. We implement EnC-IoT with two schemes in IPFS based on a decentralized IoT environment. The experimental results show that EnC-IoT provides a performance improvement of up to 100.7× for uploading and 9.6× for downloading compared with the existing systems.

This paper introduces a novel file system, ScaleDFS, designed to accelerate decentralized file sharing for a private network, leveraging the potential of scaling file management based on directed acyclic graph (DAG) on modern hardware. Specifically, in ScaleDFS, we first design a DAG builder that parallelizes the construction of DAG nodes for a file while preserving critical orders to speed up the uploading process. Second, we introduce a DAG reader that retrieves leaf DAG nodes in parallel without graph traversal assisted by a devised DAG cache to accelerate the downloading process. Finally, we present a DAG remover that rapidly identifies obsolete DAG nodes/data and removes them in parallel to mitigate the garbage collection overhead without compromising consistency. We implement ScaleDFS based on IPFS and demonstrate that ScaleDFS outperforms IPFS by up to 3.7×, 1.8×, and 12.6× in realistic file, private blockchain, and gateway workloads, respectively.