A model for blockchain-based privacy-preserving for big data users on the internet of thing

Abstract

Recently, with the emergence and growth of the IoT as a promising vehicle for sustainable development, the concept of ‘smart cities’ has advanced significantly. However, many challenges inhibit the development of using IoT applications in smart cities, such as issues of privacy, scalability, trust, security, and centralisation. On a daily basis in smart cities, the IoT generates a large amount of data (big data) which could potentially be used for questionable or suspect purposes by attackers. The weight of the security issues surrounding big data must be acknowledged as the associated technology is continuously developing. To solve this issue, a strategy that secures important and potentially sensitive user information on a distributed blockchain and transmits non-sensitive information to the primary system by controlling the size of the blockchain is proposed. This solution cannot be achieved in traditional blockchain because it requires too many resources. The model is composed of three proposed algorithms: the first aims to allocate data to each user; the second performs the process of searching for data, and the third confirms the communication process. Experiments have proved that this proposed protocol for blockchain has excellent byzantine fault tolerance. The final experimental results of the proposed model established that the algorithms effectively meet the performance requirements.

A survey: medical health record data security based on interplanetary file system and blockchain technologies

“…It is obviously read from several other nodes if it was already stored, conserving space in the process. Slicing huge files: IPFS nodes are unconcerned about the storage path or name of the data they store [12]. When using IPFS to slice and dice big files, many slices can be downloaded in parallel [13].…”

Insights on the internet of things: past, present, and future directions

“…IoT applications may be found in a variety of industries, including automotive, telecommunications, and energy [20], [122], [139], [140], [149]. Smart homes furnished with smart appliances, smart thermostats, and linked lighting, heating, and electrical devices, for example, may be handled remotely via computers and smartphones in the consumer market [150]- [155]. Wearable devices with sensors and software may gather and analyze user data, transmitting signals about the users to other technologies in order to make their lives simpler and more pleasant.…”

“…Deep learning is quite effective at dealing with such massive amounts of data and can generate highly accurate results. As a result, Internet of Things (IoT), big data analytics, and deep learning are all important to the growth of a high-tech society [22], [28], [74], [77], [89], [92], [93], [101], [103], [110], [114], [126], [149], [152], [155], [183], [188], [202], [232], [265], [318], [352].…”

Effective privacy preserving in cloud computing using position aware Merkle tree model

“…Identifying a provider with vulnerabilities that are easy to exploit makes them a highly visible target for cyber-criminals. Clouds lacking adequate security [6] measures become prime targets for malicious activities, given their architecture allows for simultaneous attacks on multiple websites. Without proper security, a single malicious activity could compromise numerous websites.…”