We present CRYPTFLOW, a system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build two components. Our first component is an end-to-end compiler from TensorFlow to a variety of MPC protocols. The second component is an improved semi-honest 3-party protocol that provides significant speedups for inference. We empirically demonstrate the power of our system by showing the secure inference of real-world neural networks such as DENSENET121 for detection of lung diseases from chest X-ray images and 3D-UNet for segmentation in radiotherapy planning using CT images. In particular, this paper provides the first evaluation of secure segmentation of 3D images, a task that requires much more powerful models than classification and is the largest secure inference task run till date.
We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over Image…
Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 che…
We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semi-honest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity gu…
Microsoft Research（微软研究院）；IIT Madras（印度理工学院马德拉斯分校）
ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model provider or model's weights to a user. For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital. While secure inference is in principle feasible for this setting, there are no existing techniques that make it practical at s…
Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these issues, this paper proposes a healthcare information security storage solution based on Hyperledger Fabric and the Attribute-Based Access Control (ABAC) framework. The scheme first utilizes attribute-based access control, which allows dynamic…
The rapid growth of the Internet of Things technology in healthcare domain led to the appearance of many security threats and risks. It became very challenging to provide full protection with the expansion in using sensor objects in medical field, this led to the Internet of Medical Things definition, the security part in IoMT poses a perilous problem that keeps growing, because of the data sensitivity and critical information. The lack of providing a secure environment in IoMT may lead to patients privacy issues, not only leaving the data privacy of the patients at risk but also their lives c…
Recently, an infectious disease, coronavirus disease 2019 (COVID-19), has been reported in Wuhan, China, and subsequently spread worldwide within a couple of months. On May 16, 2020, the COVID-19 pandemic has affected several countries in the world. In this article, we present how the amalgamation of blockchain, cryptography, internet of medical things (IoMT), and artificial intelligence (AI) technologies can address such an issue in the event of the COVID-19 pandemic. Further, we propose a secure AI-based blockchain-assisted IoMT (SAI-BA-IoMT) model for the healthcare system in the COVID-19 c…
In this paper, we propose an efficient digital watermarking scheme to strengthen the security level already present in the database management system and to avoid illegal access to comprehensive content of database including patient's information. Doctors diagnose medical images by seeing Region of Interest (ROI). A ROI of a medical image is an area including important information and must be stored without any distortion. If a medical image is illegally obtained or if its content is changed, it may lead to wrong diagnosis. We substitute the part out of ROI of LSB bitplane of the image with th…
Medical data belongs to whom it produces it. In an increasing manner, this data is usually processed in unauthorized third-party clouds that should never have the opportunity to access it. Moreover, recent data protection regulations (e.g., GDPR) pave the way towards the development of privacy-preserving processing techniques. In this paper, we present a proof of concept of a streaming IoT architecture that securely processes cardiac data in the cloud combining trusted hardware and Spark. The additional security guarantees come with no changes to the application's code in the server. We tested…
Processing sensitive data, such as those produced by body sensors, on third-party untrusted clouds is particularly challenging without compromising the privacy of the users generating it. Typically, these sensors generate large quantities of continuous data in a streaming fashion. Such vast amount of data must be processed efficiently and securely, even under strong adversarial models. The recent introduction in the mass-market of consumer-grade processors with Trusted Execution Environments (TEEs), such as Intel SGX, paves the way to implement solutions that overcome less flexible approaches,…
With the ubiquitous advancement in smart medical devices and systems, the potential of Remote Patient Monitoring (RPM) network is evolving in modern healthcare systems. The medical professionals (doctors, nurses, or medical experts) can access vitals and sensitive physiological information about the patients and provide proper treatment to improve the quality of life through the RPM network. However, the wireless nature of communication in the RPM network makes it challenging to design an efficient mechanism for secure communication. Many authentication schemes have been proposed in recent yea…
With the rapid development of health equipments, increasingly more patients have installed the implantable medical devices (IMD) in their bodies for diagnostic, monitoring, and therapeutic purposes. IMDs are extremely limited in computation power and battery capacity. Meanwhile, IMDs have to communicate with an external programmer device (i.e., IMD programmer) through the wireless channel, which put them under the risk of unauthorized access and malicious wireless attacks. In this paper, we propose a proxy-based fine-grained access control scheme for IMDs, which can prolong the IMD's lifetime …
The efficiency and service quality in a medical environment can be improved by using electronic documents (or e-docs) and digital signatures to speed up both doctors' activity and to provide in the same time easy retrieval and use of needed data without loosing convenience. Our proposed solution satisfies these needs by making use of the AIDA system and many cutting-edge techniques to build a digitalized management system. To be more specific, we present firstly the AIDA document exchange framework for the management (creation, search, storage) of e-docs expressed in XML format. The framework …
To facilitate monitoring and management, modern Implantable Medical Devices (IMDs) are often equipped with wireless capabilities, which raise the risk of malicious access to IMDs. Although schemes are proposed to secure the IMD access, some issues are still open. First, pre-sharing a long-term key between a patient's IMD and a doctor's programmer is vulnerable since once the doctor's programmer is compromised, all of her patients suffer; establishing a temporary key by leveraging proximity gets rid of pre-shared keys, but as the approach lacks real authentication, it can be exploited by nearby…
Quantum computing research might lead to "quantum leaps," and it could have unanticipated repercussions in the medical field. This technique has the potential to be used in a broad range of contexts, some of which include the development of novel drugs, the individualization of medical treatments, and the speeding of DNA sequencing. This work has assembled a list of the numerous methodologies presently employed in quantum medicine and other disciplines pertaining to healthcare. This work has created a list of the most critical concerns that need to be addressed before the broad use of quantum …
Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e. Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this paper, we design a secure federated learning mechanism with mul…
Kyoto University（京都大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；University of Notre Dame（圣母大学）
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a service schemes while protecting data confidentiality, in this work, we propose blind UNET (BUNET), a secure protocol that implements privacy-preserving medical image segmentation based on the UNET architecture. In BUNET, we efficiently utilize cryptographic primitives such as homomorphic encryption and garbled circuits (GC) to design a complete secure protocol for the UNET neural architecture. In addition, we perform…
Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research …
The Internet is a ubiquitous and affordable communications network suited for e-commerce and medical image communications. Security has become a major issue as data communication channels can be intruded by intruders during transmission. Though, different methods have been proposed and used to protect the transmission of data from illegal and unauthorized access, code breakers have come up with various methods to crack them. DNA based Cryptography brings forward a new hope for unbreakable algorithms. This paper outlines an encryption scheme with DNA technology and JPEG Zigzag Coding for Secure…
Institute for Artificial Intelligence in Medicine, University Hospital Essen（埃森大学医院医学人工智能研究所）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Cancer Research Center Cologne Essen（科隆-埃森癌症研究中心）
AI/Computing at scale is a difficult problem, especially in a health care setting. We outline the requirements, planning and implementation choices as well as the guiding principles that led to the implementation of our secure research computing enclave, the Essen Medical Computing Platform (EMCP), affiliated with a major German hospital. Compliance, data privacy and usability were the immutable requirements of the system. We will discuss the features of our computing enclave and we will provide our recipe for groups wishing to adopt a similar setup.
IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed an…
Integration of Federated Learning and Blockchain in Healthcare: A Tutorial
Montreal Blockchain Lab, University of Montreal（蒙特利尔区块链实验室，蒙特利尔大学）；School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气与计算机工程学院）
Implantable medical devices (IMDs), such as pacemakers, implanted cardiac defibrillators, and neurostimulators are medical devices implanted into patients' bodies for monitoring physiological signals and performing medical treatments. Many IMDs have built-in wireless communication modules to facilitate data collecting and device reprogramming by external programmers. The wireless communication brings significant conveniences for advanced applications such as real-time and remote monitoring but also introduces the risk of unauthorized wireless access. The absence of effective access control mec…
In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several hospitals might wish to share patient medical records for enhanced diagnostics and disease prediction, but may not be able to share data in the clear because of privacy concerns. In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations. Recent works on the problem have mainly focused on passively secure protocols, whose security holds against passive (`semi…
In medical emergencies, an instant and secure messaging is an important service to provide quality healthcare services. A session initiation protocol (SIP) is an IP-based multimedia and telephony communication protocol used to provide instant messaging services. Thus, design of secure and efficient SIP for quality medical services is an emerging problem. In this paper, we first explore the security limitations of the existing SIPs proposed by Sureshkumar et al. and Zhang et al. in the literature. Our analysis shows that most of the existing schemes fail to protect the user credentials when une…
Over the past decade, focus on the security and privacy aspects of implantable medical devices (IMDs) has intensified, driven by the multitude of cybersecurity vulnerabilities found in various existing devices. However, due to their strict computational, energy and physical constraints, conventional security protocols are not directly applicable to IMDs. Custom-tailored schemes have been proposed instead which, however, fail to cover the full spectrum of security features that modern IMDs and their ecosystems so critically require. In this paper we propose IMDfence, a security protocol for IMD…
Over the years health care has seen major improvement due to the introduction information and communication technology with electronic medical prescription being one the areas benefiting from it. Within the overall context of protection of health care information, privacy of prescription data needs special treatment. This paper presents an e-prescription system that addresses some challenges pertaining to the prescription privacy protection in the process of drug prescription. The developed system uses spread spectrum image steganography algorithm with Advanced Encryption Standard (AES) key im…
Artificial Intelligence (AI) incorporating genetic and medical information have been applied in disease risk prediction, unveiling disease mechanism, and advancing therapeutics. However, AI training relies on highly sensitive and private data which significantly limit their applications and robustness evaluation. Moreover, the data access management after sharing across organization heavily relies on legal restriction, and there is no guarantee in preventing data leaking after sharing. Here, we present Genie, a secure AI platform which allows AI models to be trained on medical data securely. T…
The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services. This paper presents a novel smart and secure Healthcare system (ssHealth), which, leveraging advances in edge computing and blockchain technologies, permits epidemics discovering, remote monitoring, and fast emergency response. The proposed system also allows for secure medical d…
Background: The kidney exchange problem (KEP) addresses the matching of patients in need for a replacement organ with compatible living donors. Ideally many medical institutions should participate in a matching program to increase the chance for successful matches. However, to fulfill legal requirements current systems use complicated policy-based data protection mechanisms that effectively exclude smaller medical facilities to participate. Employing secure multi-party computation (MPC) techniques provides a technical way to satisfy data protection requirements for highly sensitive personal he…
Many organizations stand to benefit from pooling their data together in order to draw mutually beneficial insights -- e.g., for fraud detection across banks, better medical studies across hospitals, etc. However, such organizations are often prevented from sharing their data with each other by privacy concerns, regulatory hurdles, or business competition. We present Senate, a system that allows multiple parties to collaboratively run analytical SQL queries without revealing their individual data to each other. Unlike prior works on secure multi-party computation (MPC) that assume that all part…
University Hospital Tübingen（图宾根大学医院）；University of Tübingen（图宾根大学）
The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an 'algorithm to the data' paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-…
Timely sharing of electronic health records (EHR) across providers is essential and significance in facilitating medical researches and prompt patients' care. With sharing, it is crucial that patients can control who can access their data and when, and guarantee the security and privacy of their data. In current literature, various system models, cryptographic techniques and access control mechanisms are proposed which requires patient's consent before sharing. However, they mostly focus on patient is available to authorize the access of the EHR upon requested. This is impractical given that t…
In the cancerology domain, we were brought to make periodic mammography images to monitor tumor patients. Oracle Database Management system (DBMS) is a solution to manage these images with patient's data recorder. Knowing the large size of medical images of mammograms, the Oracle DBMS saves these images outside the Oracle database using external LOBs. The link between these images and Oracle is done through the BFILE. At this level, two problems are raised: the first problem is that access to these images can become impossible because the link is likely to be broken. The second problem is secu…
Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne（量子云计算与分布式系统实验室，计算机与信息系统学院，墨尔本大学）；Department of Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus（电气与电子工程系，比特斯-帕利尼学院）；Department of Computer Science and Information Systems, BITS-Pilani, Pilani Campus（计算机科学与信息系统系，比特斯-帕利尼学院）
