Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Funding information National Science Foundation, Grant/Award Number: 1619346, 1559483, 1718956 and 1730650 Abstract Today, patients are demanding a newer and more sophisticated health care system, one that is more personalized and matches the speed of modern life. For the latency and energy efficiency requirements to be met for a real-time collection and analysis of health data, an edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Previous health care surveys have focused on new fog architecture and sensor types, which leaves untouched the aspect of optimal computing techniques, such as encryption, authentication, and classification that are used on the devices deployed in an edge computing architecture. This paper aims first to survey the current and emerging edge computing architectures and techniques for health care applications, as well as to identify requirements and challenges of devices for various use cases. Edge computing application primarily focuses on the classification of health data involving vital sign monitoring and fall detection. Other low-latency applications perform specific symptom monitoring for diseases, such as gait abnormalities in Parkinson's disease patients. We also present our exhaustive review on edge computing data operations that include transmission, encryption, authentication, classification, reduction, and prediction. Even with these advantages, edge computing has some associated challenges, including requirements for sophisticated privacy and data reduction methods to allow comparable performance to their Cloud-based counterparts, but with lower computational complexity. Future research directions in edge computing for health care have been identified to offer a higher quality of life for users if addressed.

Edge computing in smart health care systems: Review, challenges, and research directions

The Internet of Things (IoT) becomes a novel paradigm as more and more devices are connected to the Internet, enabling several innovative applications such as smart home, industrial automation, and connected health. However, the cyber-attack to these applications is a big issue and countermeasures are in dire need to provide system security and user privacy. In this paper, we address the traffic analysis attack to smart homes, where adversaries intercept the Internet traffic from/to the smart home gateway and profile residents’ behaviors through digital traces. Traditional cryptographic tools may not work well due to the effectiveness of adversaries’ machine learning algorithms in classifying encrypted traffic, so here we propose a privacy-preserving traffic obfuscation framework to achieve the goal. To be specific, we leverage the smart community network of wirelessly connected smart homes and intentionally direct each smart home’s traffic to another home gateway before entering the Internet. The design jointly considers the network energy consumption and the resource constraints in IoT devices, while achieving strong differential privacy guarantee so that adversaries cannot link any traffic flow to a specific smart home. Besides, we consider a hostile smart community network and develop secure multihop routing protocols to guarantee the source/destination unlinkability and satisfy each user’s personalized privacy requirement. To evaluate the effectiveness of our framework in protecting privacy and reducing network energy consumption, extensive simulations are conducted and the results demonstrate that our design outperforms other differential privacy mechanism in preserving privacy and minimizing network utility cost.

EPIC: A Differential Privacy Framework to Defend Smart Homes Against Internet Traffic Analysis

Healthcare monitoring systems in hospitals and other health centers have witnessed throughout the last decade a tremendous growth and wireless healthcare monitoring devices with various technologies have become of great interest in many nations around the world. The proposed paper aims at integrating artificial intelligence technology, such as the neural networks and fuzzy system in a secure healthcare monitoring system in order to enable the system to work as a smart healthcare model that decides the priority by itself depending on the collected health parameters from the senor nodes. The proposed model consists of a trust environment that is responsible for collecting authenticated physiological data from patient's body which is then sent through GSM module to Azure IoT Hub where raw data is converted into linguistic representation , with the help of logic-based algorithm, which is trained in fuzzy-based inference system (FBIS) to get the status of the patient. The proposed system, then, provides reliable, accurate, secure and real-time patient monitoring. The following sections describe how the FBIS can be integrated with a secure healthcare monitoring system to get the states of the patient and send it to the medical advisory for the preliminary precautions.

Secure IoT communications for smart healthcare monitoring system

1Health Law and Policy Programs, Université de Sherbrooke, Sherbrooke, QC, Canada 2Center for Genomics and Policy (CGP), McGill University, Montreal, QC, Canada 3Université de Montréal, Montreal, QC, Canada 4Université Lille 2, Lille, France 5University of Sherbrooke, Sherbrooke, QC, Canada 6Department of Human Genetics Cross-Appointed at the Bioethics Unit, McGill University, Montreal, QC, Canada *Corresponding author. E-mail: hortense.gallois@gmail.com, yann.joly@mcgill.ca

/pdf/integrating-artificial-intelligence-into-health-care-through-4w3vw5bwgn.pdf

Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers?

Current advances in connectivity, sensor technology, computing power and the development of complex algorithms for processing health-related data are paving the way for the delivery of innovative long-term health care services in the future. Such technological developments will, in particular, assist the elderly and infirm to live independently, at home, for much longer periods. The home is, in fact, becoming a locus for health care innovation that may in the future compete with the hospital. However, along with these advances come valid privacy and security questions arising from the fact that the data collected and transmitted through these technologies could also allow for individual monitoring as well as unauthorized access to critical diagnostic and other health data.

/pdf/remote-home-health-care-technologies-how-to-ensure-privacy-3rabx8qqkm.pdf

Remote home health care technologies: how to ensure privacy? Build it in: Privacy by Design

We live in a world of constant data flow, and safeguarding data has never been more important Be it medical records, financial information or simple online passwords, the amount of private data that needs to be protected continues to grow Along with this growth in the need to secure data, however, are the privacy concerns people have with their data While some would pit security and privacy against each other, arguing that individuals must choose one over the other, the two actually can and should reinforce each other It’s this model that forms the basis of this chapter: Privacy and security should be pursued hand-in-hand as we move toward an increasingly connected, digital world To fully realize the benefits of information technology, big data and IoT, individuals must be confident that their devices are designed in a way that protects their data and that any data being collected and processed from those devices is used responsibly Using internationally recognized mechanisms such as the Fair Information Privacy Principles, public and private organizations can enable both the innovative AND ethical use of data The key is not avoiding data but using it mindfully It takes data to protect data

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2973280_code1750077.pdf?abstractid=2973280&mirid=1

It Takes Data to Protect Data

Millions of PCs are currently sold equipped with a Trusted Platform Module, TPM, serving as a root of trust on the platform. Trusted Computing as an area of security has acquired significant visibility, and many new products and a growing number of research projects in areas ranging from virtualization to network security are based on Trusted Computing technologies and vision. In order to fully realize the vision of the Trusted Computing community, dedicated or compatible trust infrastructure for verification and attestation is required. Similar to other trust-enabling technologies, Trusted Computing needs an infrastructure that can verify the claim that a device is genuine and can be trusted to take part in a transaction, in which it is involved. Such an infrastructure will enable an environment where individuals can use the technology for protected transactions and potentially employ less risky authentication methods. This paper explores the role of infrastructure in Trusted Computing, starting with the discussion of the infrastructure’s importance and issues in trust establishment, followed by the description of the basics of Trusted Computing functionality requiring infrastructure support. We use examples of other trust enabling infrastructures, such as general-purpose PKI and infrastructure for Identity Federation to highlight common approaches. Finally, we touch upon economics of trust and intermediation, in order to define potential models for building enabling infrastructure for Trusted Computing.. While the paper doesn’t propose concrete solutions for the infrastructure problem in Trusted Computing, some possible avenues of building the necessary framework are outlined.

Infrastructure for Trusted Environment: In Search of a Solution

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Remote home health care technologies: how to ensure privacy? Build it in: Privacy by Design

It Takes Data to Protect Data

Infrastructure for Trusted Environment: In Search of a Solution