Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

A HYBRIDIZED ENCRYPTION SCHEME BASED ON ELLIPTIC CURVE CRYPTOGRAPHY FOR SECURING DATA IN SMART HEALTHCARE

Recent developments in smart healthcare have brought us a great deal of convenience. Connecting common objects to the Internet is made possible by the Internet of Things (IoT). These connected gadgets have sensors and actuators for data collection and transfer. However, if users' private health information is compromised or exposed, it will seriously harm their privacy and may endanger their lives. In order to encrypt data and establish perfectly alright access control for such sensitive information, attribute-based encryption (ABE) has typically been used. Traditional ABE, however, has a high processing overhead. As a result, an effective security system algorithm based on ABE and Fully Homomorphic Encryption (FHE) is developed to protect health-related data. ABE is a workable option for one-to-many communication and perfectly alright access management of encrypting data in a cloud environment. Without needing to decode the encrypted data, cloud servers can use the FHE algorithm to take valid actions on it. Because of its potential to provide excellent security with a tiny key size, elliptic curve cryptography (ECC) algorithm is also used. As a result, when compared to related existing methods in the literature, the suggested hybridized algorithm (ABE-FHE-ECC) has reduced computation and storage overheads. A comprehensive safety evidence clearly shows that the suggested method is protected by the Decisional Bilinear Diffie-Hellman postulate. The experimental results demonstrate that this system is more effective for devices with limited resources than the conventional ABE when the system’s performance is assessed by utilizing standard model

効率的で安全な集合間類似結合に関する研究

筑波大学 (University of Tsukuba)201

Reconsidering big data security and privacy in cloud and mobile cloud systems

Large scale distributed systems in particular cloud and mobile cloud deployments provide great services improving people\u27s quality of life and organizational efficiency. In order to match the performance needs, cloud computing engages with the perils of peer-to-peer (P2P) computing and brings up the P2P cloud systems as an extension for federated cloud. Having a decentralized architecture built on independent nodes and resources without any specific central control and monitoring, these cloud deployments are able to handle resource provisioning at a very low cost. Hence, we see a vast amount of mobile applications and services that are ready to scale to billions of mobile devices painlessly. Among these, data driven applications are the most successful ones in terms of popularity or monetization. However, data rich applications expose other problems to consider including storage, big data processing and also the crucial task of protecting private or sensitive information. In this work, first, we go through the existing layered cloud architectures and present a solution addressing the big data storage. Secondly, we explore the use of P2P Cloud System (P2PCS) for big data processing and analytics. Thirdly, we propose an efficient hybrid mobile cloud computing model based on cloudlets concept and we apply this model to health care systems as a case study. Then, the model is simulated using Mobile Cloud Computing Simulator (MCCSIM). According to the experimental power and delay results, the hybrid cloud model performs up to 75% better when compared to the traditional cloud models. Lastly, we enhance our proposals by presenting and analyzing security and privacy countermeasures against possible attacks