P^2-SWAN: Real-Time Privacy Preserving Computation for IoT Ecosystems

Sensitive personal user-data collected by Internet-of-Things (IoT) devices is vulnerable to information leaks when uploaded to third-party cloud computing infrastructures. Even though data is encrypted before being sent, to perform analyses on the received data, the computing infrastructure typically decrypts the data, and then performs computation. Therefore, during computation, data can be leaked by means of honest-but-curious system administrators. To overcome this vulnerability, homomorphic encryption enables "blind" computation directly on encrypted data, thus rendering obsolete any data leaks. However, homomorphic encryption is highly resource demanding, as it performs many compute-intensive operations during encryption, while also increasing the size of ciphertexts, which makes it unsuitable for low-powered (mobile) IoT devices. For similar reasons, performing operations on encrypted data is also a challenging task, especially when real-time decision-making is needed. In such scenarios, efficient solutions must be augmented by placing computation close to the data: at the network edge. In this paper, we introduce privacy preserving SWAN (P2-SWAN), a homomorphic-encryption enabled mobile computing framework. Even though such encryption adds significant computational overhead, our evaluation shows that it is feasible on low-powered (mobile) devices. The overhead induced on such devices is minimized due to our carefully crafted implementation. We show that performing encrypted operations achieves excellent scalability, thus only modest numbers of computing servers can handle the load for data generated by millions of devices. Furthermore, our proposed approach achieves real-time computation not only for encrypting data on mobile devices, but also for performing encrypted computation.