PETRIoT - A Privacy Enhancing Technology Recommendation Framework for IoT Computing

Data sharing has become a critical component in any computing domain for organizations of different scales. Governments and organizations often must share their sensitive data with third parties in order to analyze, mine or fine tune data for critical operations. However, this can lead to privacy concerns when dealing with sensitive data. Privacy Enhancing Techniques (PETs) allow data sharing between two or more parties, while protecting the privacy of the data. There are different types of PETs that offer different advantages and disadvantages for specific application domains. Therefore, it is imperative that a careful selection and matching of application domain and PET is exercised. Selection of PETs becomes more critical when it comes to the data generated from Internet of Things (IoT) devices as such devices are becoming more pervasively present in our lives and thus, capturing more sensitive information. In this paper, we design a novel framework in accordance with National Institute of Standards and Technology (NIST) recommendations to select an appropriate PET in different application settings with respect to privacy, computational cost and usability. We design a recommendation system based on a strategy which requires input from data owners and end users. On the basis of the responses selected, the recommendation is made for an appropriate PET to be deployed in a given IoT application.

Privacy Preserving Data Sharing, IoT Devices, Privacy Enhancing Techniques, Differential Privacy, Federated Learning, Data De-Identification, Homomorphic Encryption, Multiparty Computation, Synthetic Data Generation.

Conference (external link, opens in new window)