Machine Learning as a Service (MLaaS) is an innovative framework that enables a broad range of users to capitalize on the powerful Artificial Intelligence (AI) technologies. Nevertheless, MLaaS raises a privacy concern for both the client data and server model. To address this issue, several Secure Inference (SI) frameworks for MLaaS have been proposed in the literature that take advantage of Homomorphic Encryption (HE) operations. However, the computation cost of these frameworks is still high, especially for real-time applications. In this paper, we propose a novel system called input structure optimization for neural networks (TILE) to accelerate SI. The goal of TILE is to reduce both linear and non-linear computation costs, as well as non-linear communication costs in MLaaS, while maintaining the model accuracy. TILE defines two novel HE-friendly input structures: Internal Tile and External Tile Structures, aimed at reducing the HE operations for SI. We also develop a search mechanism to identify optimal application locations for these input structures. We apply TILE to widely used models such as VGG and ResNet, and datasets including Cifar10 and Tiny-ImageNet. The experimental results demonstrate that TILE effectively reduces the computation time, with up to 51.57% reduction for a state-of-the-art SI framework. Furthermore, TILE can also be applied to models that have already been pruned to significantly reduce the computation time, to further reduce the overall computation time by 25.90%.

The pandemics, such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this article, we develop a novel privacy-preserved pandemic tracing system, privacy-preserved pandemic infection risk computation (PRISC), to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties: 1) a mobile network operator (MNO); 2) a social network provider; and 3) the department of health. The physical contact records between users are obtained by the MNO from the users’ cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro-level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for data sets with 100000 users.

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Recent studies have utilized power side-channel in-formation to identify this intrusion behavior on IoT devices but still lack accurate models in real-time for ubiquitous botnet detection. We propose the first online intrusion detection system called DeepAuditor for multiple IoT devices via power auditing. To de-velop the real-time system, we propose a lightweight power auditing device called Power Auditor. We also design a distributed CNN classifier for online inference in a laboratory setting. In order to protect data leakage and reduce networking redundancy, we then propose a privacy-preserved inference protocol via Packed Homo-morphic Encryption and a sliding window protocol in our system. The classification accuracy and processing time are measured, and the proposed classifier outperforms a baseline classifier, especially against unseen patterns. We also demonstrate that the distributed CNN design is secure against any distributed components. Over-all, the measurements are shown to the feasibility of our real-time distributed system for intrusion detection on IoT devices.

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Thus, a new approach that monitors these ini-tial intrusions is needed. Power side-channel information can be used because it does not require any modification in programming languages or operating systems on diverse IoT devices. We propose a distributed power side-channel auditing system for online IoT intrusion detection. To meet the real-time requirement, we develop a lightweight power auditing device. We then design a distributed CNN classifier for online inference in a laboratory setting. Two distributed protocols are also proposed in order to protect data leakage and reduce networking redundancy. In this work, we demonstrate the feasibility of our real-time distributed system for intrusion detection on IoT devices.