A framework for collaborative learning in secure high-dimensional space

As the amount of data generated by the Internet of the Things (IoT) devices keeps increasing, many applications need to offload computation to the cloud. However, it often entails risks due to security and privacy issues. Encryption and decryption methods add to an already significant computational burden. In this paper, we propose a novel framework, called SecureHD, which provides a secure learning solution based on the idea of high-dimensional (HD) computing. We encode original data into secure, high-dimensional vectors. The training is performed with the encoded vectors. Thus, applications can send their data to the cloud with no security concerns, while the cloud can perform the offloaded tasks without additional decryption steps. In particular, we propose a novel HD-based classification algorithm which is suitable to handle a large amount of data that the cloud typically processes. In addition, we also show how SecureHD can recover the encoded data in a lossless manner. In our evaluation, we show that the proposed SecureHD framework can perform the encoding and decoding tasks 145.6× and 6.8× faster than a state-of-the-art encryption/decryption library running on the contemporary CPU. In addition, our learning method achieves high accuracy of 95% on average for diverse practical classification tasks including cloud-scale datasets.