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1. Communication-Efficient and Privacy-Preserving Edge-Cloud Framework For Smart Healthcare

   https://doi.org/10.1109/GCWkshps58843.2023.10464501  

   Mpembele, Armando B; Rogers, Tamara; Ghosh, Uttam; Shetty, Sachin (March 2024, IEEE)

   The healthcare industry has experienced a re-markable digital transformation through the adoption of IoT technologies, resulting in a significant increase in the volume and variety of medical data generated. Challenges in processing, analyzing, and sharing healthcare data persist. Traditional cloud computing approaches, while useful for processing healthcare data, have drawbacks, including delays in data transfer, data privacy concerns, and the risk of data unavailability. In this paper, we propose a software-defined 5G and AI-enabled distributed edge-cloud collaboration platform to classify healthcare data at the edge devices, facilitate realtime service delivery, and create AI/ML-based models for identifying patients' potential medical conditions. In our architecture, we have incorporated a federated learning scheme based on homomorphic encryption to provide privacy in data sharing and processing. The proposed framework ensures secure and efficient data communication and processing, ultimately fostering effective collaboration among healthcare institutions. The models will be validated by performing a comparative time analysis, and the interplay between edge and cloud computing will be investigated to support realtime healthcare applications. 

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2. Towards an Analysis of the Architecture, Security, and Privacy Issues in Vehicular Fog Computing

   Hoque, Mohammad Aminul; Hasan, Ragib (April 2019, Proceedings of IEEE Southeastcon)

   The vehicular fog is a relatively new computing paradigm where fog computing works with the vehicular network. It provides computation, storage, and location-aware services with low latency to the vehicles in close proximity. A vehicular fog network can be formed on-the-fly by adding underutilized or unused resources of nearby parked or moving vehicles. Interested vehicles can outsource their resources or data by being added to the vehicular fog network while maintaining proper security and privacy. Client vehicles can use these resources or services for performing computation-intensive tasks, storing data, or getting crowdsource reports through the proper secure and privacy-preserving communication channel. As most vehicular network applications are latency and location sensitive, fog is more suitable than the cloud because of the capability of performing calculations with low latency, location awareness, and the support of mobility. Architecture, security, and privacy models of vehicular fog are not well defined and widely accepted yet as it is in its early stage. In this paper, we have analyzed existing studies on vehicular fog to determine the requirements and issues related to the architecture, security, and privacy of vehicular fog computing. We have also identified and highlighted the open research problems in this promising area. 

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3. Using science fiction texts to surface user reflections on privacy

   https://doi.org/10.1145/3123024.3123080  

   Wong, Richmond Y.; Mulligan, Deirdre K.; Chuang, John (October 2017, Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers)

   We explore how an excerpt from a science fiction novel describing a near-future miniature wireless streaming camera technology can be used to elicit privacy concerns from participants. We conduct an online experiment (n=151) to compare participants' responses to a narrative fiction passage and a "plain" functional description of the same imagined technology. Qualitatively we find that participants with the fiction passage raised concerns about different types of privacy harms and were more likely to suggest design modifications to protect privacy. Quantitatively, we find that participants with the fiction passage provided higher ratings of negative affect, and lower ratings of comfort and acceptability. This suggests that researchers trying to understand users' privacy concerns with new ubiquitous computing technologies may benefit from presenting the technology in multiple formats to elicit a broader range of values reflections. 

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4. PAMS: Improving Privacy in Audio-Based Mobile Systems

   https://doi.org/10.1145/3417313.3429383  

   Xia, Stephen; Jiang, Xiaofan (November 2020, Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things)

   null (Ed.)

   Smartphones and mobile applications have become an integral part of our daily lives. This is reflected by the increase in mobile devices, applications, and revenue generated each year. However, this growth is being met with an increasing concern for user privacy, and there have been many incidents of privacy and data breaches related to smartphones and mobile applications in recent years. In this work, we focus on improving privacy for audio-based mobile systems. These applications will generally listen to all sounds in the environment and may record privacy-sensitive signals, such as speech, that may not be needed for the application. We present PAMS, a software development package for mobile applications. PAMS integrates a novel sound source filtering algorithm called Probabilistic Template Matching to generate a set of privacy-enhancing filters that remove extraneous sounds using learned statistical "templates" of these sounds. We demonstrate the effectiveness of PAMS by integrating it into a sleep monitoring system, with the intent to remove extraneous speech from breathing, snoring, and other sleep sounds that the system is monitoring. By comparing our PAMS enhanced sleep monitoring system with existing mobile systems, we show that PAMS can reduce speech intelligibility by up to 74.3% while maintaining similar performance in detecting sleeping sounds. 

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5. Privacy-Preserving Policy Synthesis in Markov Decision Processes

   https://doi.org/10.1109/CDC42340.2020.9304015  

   Gohari, Parham; Hale, Matthew; Topcu, Ufuk (December 2020, Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC))

   null (Ed.)

   In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation’s investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protects the privacy of the transition probabilities in a Markov decision process. We use differential privacy as the mathematical definition of privacy. The algorithm first perturbs the transition probabilities using a mechanism that provides differential privacy. Then, based on the privatized transition probabilities, we synthesize a policy using dynamic programming. Our main contribution is to bound the "cost of privacy," i.e., the difference between the expected total rewards with privacy and the expected total rewards without privacy. We also show that computing the cost of privacy has time complexity that is polynomial in the parameters of the problem. Moreover, we establish that the cost of privacy increases with the strength of differential privacy protections, and we quantify this increase. Finally, numerical experiments on two example environments validate the established relationship between the cost of privacy and the strength of data privacy protections. 

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