Authenticable Distributed Homomorphic Private Counter and its application in data analysis of edge computing

The rapid proliferation of advanced technologies, including the Internet of Things (IoT), cloud computing, and edge computing, has led to an exponential growth in structured and unstructured data, generated and collected across diverse applications. It is important to develop secure techniques that can efficiently process large volumes of data while preserving privacy. Privacy-preserving data analytics on encrypted data have gained popularity for performing essential calculations within cloud storage servers. However, applying these techniques to fully homomorphic encryption introduces inefficiencies and computational overheads. While homomorphic encryption allows for delegated execution of arithmetic operations directly on ciphertexts via cloud services, ensuring both efficiency and correctness in data computations remains a challenging endeavor. Most existing studies overlook simultaneous data aggregation while maintaining integrity and privacy for analytical purposes. In response, we propose an Authenticable Distributed Homomorphic Private Counter Scheme (ADHPC) for privacy-preserving data analysis in cloud computing. Our scheme securely and efficiently aggregates encrypted data within distributed edge computing environments, subsequently allowing authorized parties to decrypt and validate it. To authenticate the encrypted data, we employ an authenticable additive homomorphic encryption scheme based on online and offline setup stages. We demonstrate the applicability and efficiency of our proposed approach through implementation results and a comprehensive security analysis.