A Review of Privacy-Preserving Data Sharing and Collaboration in IoT Environments
Keywords:
Internet of Things, data sharingAbstract
A massive amount of data is being produced as the number of Internet of Things (IoT) devices used increases. To support new applications and services, this data can be shared and evaluated. Sharing sensitive data, however, presents serious privacy issues, especially in IoT contexts where data is frequently produced by personal devices. In this research, we suggest a privacy-respecting paradigm for cooperative data exchange in IoT situations. The suggested approach combines differential privacy approaches with safe multi-party computing to facilitate collaborative data sharing while preserving user privacy. Data security is maintained during computations thanks to multi-party computation, and differential privacy makes it challenging to locate a specific individual's data in a shared dataset. We analyzed several research and their implementation of each method to show the viability of our methodology. The findings demonstrate that the suggested framework may support data exchange and collaboration in IoT environments while preserving user privacy. This framework has a lot of potential to support brand-new services and applications in this industry. This tackles issues like preserving individual privacy while also enabling the study of massive datasets that come up when data is shared across many businesses. It can also be used in settings like smart homes or wearable technology when a lot of different personal gadgets are producing data. In conclusion, the paradigm we've suggested offers a way to share data collaboratively while yet protecting user privacy in IoT environments.
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Copyright (c) 2023 Raiyan Mustafa Mulla , Ishitha Ishitha Saravan; Lilibeth Reales, Vikas Rao Naidu
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