161 research outputs found
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE
GPS: Integration of Graphene, PALISADE, and SGX for Large-scale Aggregations of Distributed Data
Secure computing methods such as fully homomorphic encryption and hardware solutions such as Intel Software Guard Extension (SGX) have been applied to provide security for user input in privacy-oriented computation outsourcing. Fully homomorphic encryption is amenable to parallelization and hardware acceleration to improve its scalability and latency, but is limited in the complexity of functions it can efficiently evaluate. SGX is capable of arbitrarily complex calculations, but due to expensive memory paging and context switches, computations in SGX are bound by practical limits. These limitations make either of fully homomorphic encryption or SGX alone unsuitable for large-scale multi-user computations with complex intermediate calculations.
In this paper, we present GPS, a novel framework integrating the Graphene, PALISADE, and SGX technologies. GPS combines the scalability of homomorphic encryption with the arbitrary computational abilities of SGX, forming a more functional and efficient system for outsourced secure computations with large numbers of users. We implement GPS using linear regression training as an instantiation, and our experimental results indicate a base speedup of 1.03x to 8.69x (depending on computation parameters) over an SGX-only linear regression training without multithreading or hardware acceleration. Experiments and projections show improvements over the SGX-only training of 3.28x to 10.43x using multithreading and 4.99x to 12.67 with GPU acceleration
Fastplay-A Parallelization Model and Implementation of SMC on CUDA based GPU Cluster Architecture
We propose a four-tiered parallelization model for acceleration of the secure multiparty computation (SMC) on the CUDA based Graphic Processing Unit (GPU) cluster architecture. Specification layer is the top layer, which adopts the SFDL of Fairplay for specification of secure computations. The SHDL file generated by the SFDL compiler of Fairplay is used as inputs to the function layer, for which we developed both multi-core and GPU based control functions for garbling of various types of Boolean gates, and ECC-based 1-out-of-2 Oblivious Transfer (OT). These high level control functions invoke computation of 3-DGG (3-DES gate garbling), EGG (ECC based gate garbling), and ECC based OT that run at the secure protocol layer. An ECC Arithmetic GPU Library (EAGL), which co-run on the GPU cluster and its host, manages utilization of GPUs in parallel computing of ECC arithmetic. Experimental results show highly linear acceleration of ECC related computations when the system is not overloaded; When running on a GPU cluster consisted of 6 Tesla C870 devices, with GPU devices fully loaded with over 3000 execution threads, Fastplay achieved 35~40 times of acceleration over a serial implementation running on a 2.53GHz duo core CPU and 4GB memory. When the execution thread count exceeds this number, the speed up factor remains fairly constant, yet slightly increased
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
PIM-Enclave: Bringing Confidential Computation Inside Memory
Demand for data-intensive workloads and confidential computing are the
prominent research directions shaping the future of cloud computing. Computer
architectures are evolving to accommodate the computing of large data better.
Protecting the computation of sensitive data is also an imperative yet
challenging objective; processor-supported secure enclaves serve as the key
element in confidential computing in the cloud. However, side-channel attacks
are threatening their security boundaries. The current processor architectures
consume a considerable portion of its cycles in moving data. Near data
computation is a promising approach that minimizes redundant data movement by
placing computation inside storage. In this paper, we present a novel design
for Processing-In-Memory (PIM) as a data-intensive workload accelerator for
confidential computing. Based on our observation that moving computation closer
to memory can achieve efficiency of computation and confidentiality of the
processed information simultaneously, we study the advantages of confidential
computing \emph{inside} memory. We then explain our security model and
programming model developed for PIM-based computation offloading. We construct
our findings into a software-hardware co-design, which we call PIM-Enclave. Our
design illustrates the advantages of PIM-based confidential computing
acceleration. Our evaluation shows PIM-Enclave can provide a side-channel
resistant secure computation offloading and run data-intensive applications
with negligible performance overhead compared to baseline PIM model
Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support
While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities
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