skip to main content

Explore Research Products in the NSF-PAR

Title: Towards Strengthening the Security of Healthcare Devices using Secure Configuration Provenance
In modern healthcare, smart medical devices are used to ensure better and informed patient care. Such devices have the capability to connect to and communicate with the hospital's network or a mobile application over wi-fi or Bluetooth, allowing doctors to remotely configure them, exchange data, or update the firmware. For example, Cardiovascular Implantable Electronic Devices (CIED), more commonly known as Pacemakers, are increasingly becoming smarter, connected to the cloud or healthcare information systems, and capable of being programmed remotely. Healthcare providers can upload new configurations to such devices to change the treatment. Such configurations are often exchanged, reused, and/or modified to match the patient's specific health scenario. Such capabilities, unfortunately, come at a price. Malicious entities can provide a faulty configuration to such devices, leading to the patient's death. Any update to the state or configuration of such devices must be thoroughly vetted before applying them to the device. In case of any adverse events, we must also be able to trace the lineage and propagation of the faulty configuration to determine the cause and liability issues. In a highly distributed environment such as today's hospitals, ensuring the integrity of configurations and security policies is difficult and often requires a complex setup. As configurations propagate, traditional access control and authentication of the healthcare provider applying the configuration is not enough to prevent installation of malicious configurations. In this paper, we argue that a provenance-based approach can provide an effective solution towards hardening the security of such medical devices. In this approach, devices would maintain a verifiable provenance chain that would allow assessing not just the current state, but also the past history of the configuration of the device. Also, any configuration update would be accompanied by its own secure provenance chain, allowing verification of the origin and lineage of the configuration. The ability to protect and verify the provenance of devices and configurations would lead to better patient care, prevent malfunction of the device due to malicious configurations, and allow after-the-fact investigation of device configuration issues. In this paper, we advocate the benefits of such an approach and sketch the requirements, implementation challenges, and deployment strategies for such a provenance-based system.  more » « less
Award ID(s):
1642078
NSF-PAR ID:
10400174
Author(s) / Creator(s):
Date Published:
Journal Name:
2022 IEEE International Conference on Digital Health (ICDH)
Page Range / eLocation ID:
228 to 233
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , 2020 9th Mediterranean Conference on Embedded Computing (MECO))
    null (Ed.)
    Smart bracelets able to interpret the wearer's emotional state and communicate it to a remote decision-support facility will have broad applications in healthcare, elder care, the military, and other fields. While there are existing commercial embedded devices, such as the Apple Watch, that have health-monitoring sensors, such devices cannot sufficiently support a real-time health-monitoring system with battery-efficient remote data delivery. Ongoing R&D is developing solutions capable of monitoring multiple psycho-physiological signals. Possible hardware configurations include wrist-worn devices and sensors across an augmented reality headset (e.g., HoloLens 2). The device should carry an array of sensors of psycho-physiological signals, including a galvanic skin response sensor, motion sensor, skin temperature sensor, and a heart rate sensor. Output from these sensors can be intelligently fused to monitor the affective state and to determine specific trigger events for the wearer. To enable real-time remote monitoring applications, the device needs to be low-power to allow persistent monitoring while prolonging usage before recharging. For many applications, specialized sensor arrays are required, e.g. a galvanic skin response sensor. An application-flexible device would allow adding/removing sensors and would provide a choice of communication modules (e.g., Bluetooth 5.0 low-energy vs ZigBee). Appropriate configurations of the device would support applications in military health monitoring, drug-addiction mitigation, autistic trigger monitoring, and augmented reality exploration. A configuration example is: motion sensors (3-axis accelerometers, gyroscopes, and magnetometers to track steps, falls, and energy usage), a heart-rate sensor (e.g., an optical-based heart rate sensor with a single monitoring zone using the process of photoplethysmography (PPS)), at least a Bluetooth 5.0 (but a different communication device may be needed depending on the use case), and flash memory to temporarily store data when the device is not remotely communicating. The wearables field has greatly advanced in the quality of sensors; the fusion of multi-sensor data is the current frontier. 
    more » « less
  2. ( , Journal of hospital management and health policy)
    Reddy, S. ; Winter, J.S. ; Padmanabhan, S. (Ed.)
    AI applications are poised to transform health care, revolutionizing benefits for individuals, communities, and health-care systems. As the articles in this special issue aptly illustrate, AI innovations in healthcare are maturing from early success in medical imaging and robotic process automation, promising a broad range of new applications. This is evidenced by the rapid deployment of AI to address critical challenges related to the COVID-19 pandemic, including disease diagnosis and monitoring, drug discovery, and vaccine development. At the heart of these innovations is the health data required for deep learning applications. Rapid accumulation of data, along with improved data quality, data sharing, and standardization, enable development of deep learning algorithms in many healthcare applications. One of the great challenges for healthcare AI is effective governance of these data—ensuring thoughtful aggregation and appropriate access to fuel innovation and improve patient outcomes and healthcare system efficiency while protecting the privacy and security of data subjects. Yet the literature on data governance has rarely looked beyond important pragmatic issues related to privacy and security. Less consideration has been given to unexpected or undesirable outcomes of healthcare in AI, such as clinician deskilling, algorithmic bias, the “regulatory vacuum”, and lack of public engagement. Amidst growing calls for ethical governance of algorithms, Reddy et al. developed a governance model for AI in healthcare delivery, focusing on principles of fairness, accountability, and transparency (FAT), and trustworthiness, and calling for wider discussion. Winter and Davidson emphasize the need to identify underlying values of healthcare data and use, noting the many competing interests and goals for use of health data—such as healthcare system efficiency and reform, patient and community health, intellectual property development, and monetization. Beyond the important considerations of privacy and security, governance must consider who will benefit from healthcare AI, and who will not. Whose values drive health AI innovation and use? How can we ensure that innovations are not limited to the wealthiest individuals or nations? As large technology companies begin to partner with health care systems, and as personally generated health data (PGHD) (e.g., fitness trackers, continuous glucose monitors, health information searches on the Internet) proliferate, who has oversight of these complex technical systems, which are essentially a black box? To tackle these complex and important issues, it is important to acknowledge that we have entered a new technical, organizational, and policy environment due to linked data, big data analytics, and AI. Data governance is no longer the responsibility of a single organization. Rather, multiple networked entities play a role and responsibilities may be blurred. This also raises many concerns related to data localization and jurisdiction—who is responsible for data governance? In this emerging environment, data may no longer be effectively governed through traditional policy models or instruments. 
    more » « less
  3. ; ; ( , 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob))
    Modern medical devices aim at providing invasive e-health care services to patients with long-term conditions. Typically, these services are implemented as embedded software applications that remotely and automatically control the opera- tions of the devices according to the patient’s condition as mon- itored by the underlying sensors. Such applications are neither safe nor secure mainly because of unreliable sensors, which may provide incorrect input data either due to its malfunctioning or due to some accidental (by privileged user) or intentional (by adversary) interference. Hence, the incorrect sensor data may lead to identification of inaccurate patient condition, which may threaten the patient’s life. To ensure safety and security of e- health applications, current approaches employ data analysis techniques to monitor sensor data and alarm when some unusual value is detected and employ access control strategies to ensure that controller decisions are consistent with sensor input data. However, such approaches fail to detect stealthy attacks, e.g. bad data (false data injection) and bad computations because they do not understand what the application or device is trying to do. To this end, we evaluate our existing approach (i.e., ARMET) to assure safety and security of an emerging and critically real-time application domain of e-health. The approach is based on the specification of the application and device, which has a design and a run-time component. Given an application specification, the design component employs logical verification methods to assure that the application design is resilient to some bad data, i.e., there are no sensor input data values with meaningful threshold which are admissible to the specification but are not true. Given the specification, the runtime component monitors application’s execution and assures that the execution is consistent with the specification and alarms whenever it detects a violation, i.e., there is a bad computation. We evaluate the methodology through its application to an example medical e-health application that controls and monitors blood glucose through an insulin pump. 
    more » « less
  4. ; ; ( , IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS))
    null (Ed.)
    The increase in cyberattacks against the healthcare system, notably Electronic Health Records (EHRs) breaches, has cost the healthcare providers more in recent years. This situation is predicted to increase in the coming years as the healthcare systems are proposing a consortium EHRs repository. Due to this reason, it is crucial to deploy solutions that can ensure the security of shared health records. More specifically, maintaining the integrity and consistency of shared EHRs becomes pertinent. In this on-going research, we propose a blockchain-based solution that facilitates a scalable and secured inter-healthcare EHRs exchange. These healthcare systems maintain their records on individual private blockchain networks, and the blockchains interact to exchange patient health history based on request. The proposed solution verifies the integrity and consistency of requests and replies from other healthcare systems. It presents them in a standard format that can be easily understood by different healthcare nodes. The verification steps guard against malicious activities on both stored and in transit EHRs from insider and outsider threat actors. We evaluate the security analysis against frequently encounter outsider and insider threats within a healthcare system. The preliminary result shows that the architecture can detect and prevent threat actors from uploading compromising EHRs into the network and prevents unauthorized retrieval of patient's information. 
    more » « less
  5. ( , 2023 7th International Conference on Medical and Health Informatics (ICMHI 2023))
    This paper presents a novel framework for creating a recoverable rare disease patient identity system using blockchain and smart contracts, decentralized identifiers (DIDs), and the InterPlanetary File System (IPFS). Smart contracts are executable code that can be written into decentralized storage such as blockchains in order to enable tamper-proof transactions of data. DIDs provide a secure, decentralized, and extensible way to create, store, and manage digital identities, while IPFS provides a distributed, immutable, and secure storage system for patient identities. Utilizing these technologies with smart contracts, we created a framework to store persistent medical records of patients. Smart contracts additionally allow account recovery without the use of any centralized authority. The framework enables healthcare providers to securely access a patient's data while maintaining the patient's ownership of their data. The paper explores the advantages of using a decentralized identity system and highlights the potential of this approach to improve the security and universality of medical records for patients with rare diseases. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email