Privacy-Preserving Techniques for Data Sharing in the Era of IoT

Abstract

The rapid expansion of Internet of Things (IoT) devices has revolutionized data generation and sharing but simultaneously introduced profound privacy concerns for both individuals and organizations. To mitigate these risks, this paper evaluates cutting-edge privacy-preserving techniques specifically designed for data exchange within the IoT framework. These techniques are categorized into cryptographic, anonymization, and differential privacy approaches, with each offering distinct advantages and limitations across diverse IoT scenarios. Cryptographic techniques, including homomorphic encryption, attribute-based encryption, secure multiparty computation, and zero-knowledge proofs, offer robust privacy protection by encrypting data during transmission and computation. However, they may incur considerable computational overhead and require sophisticated key management mechanisms. Anonymization techniques such as k-anonymity, l-diversity, t-closeness, and differential privacy-based anonymization aim to conceal sensitive information by aggregating or perturbing data. While effective in safeguarding privacy, these methods may struggle to balance privacy and data utility, leading to potential information loss or re-identification risks.