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1. SCALE-FL: Scalable Cryptography-based Aggregation with Lightweight Enclaves for Federated Learning

   Brody, Micah; Januszewicz, Antonia; Zhao, Jiachen; Koirala, Nirajan; Jung, Taeho (March 2026, IACR ePrint Archive)

   Bos, Joppe W; Celi, Sofia; Kannwischer, Matthias J (Ed.)

   Privacy-Preserving Federated Learning (PPFL) emphasizes the security and privacy of contributors' data in scenarios such as healthcare, smart grids, and the Internet of Things. However, ensuring the security and privacy throughout PPFL can be challenging, given the complexities of maintaining relationships with many users across multiple epochs. Additionally, under a threat model in which the aggregating server and corrupted users are colluding adversaries, honest users' inputs and output data must be protected at all stages. Two common tools for enforcing privacy in federated learning are Private Stream Aggregation (PSA) and Trusted Execution Environments (TEE). However, PSA-only approaches still expose the raw aggregate to the server (and thus to colluding parties). TEE-only aggregation typically incurs non-negligible per-client per-epoch overhead at scale because the TEE must handle per-client communication and maintain per-client state/key material. This paper presents SCALE-FL, a novel solution for PPFL that maintains security while achieving near-plaintext performance using a state-of-the-art PSA protocol to collect user information and a TEE to hide information about the raw aggregate. By using a PSA protocol for aggregation, we can maintain the privacy of information on the untrusted server without requiring per-user key storage or use by the TEE. Then, the aggregate is securely processed by the TEE in plaintext, without the heavy encryption required on an untrusted server. Finally, we ensure the security of user inputs in the federated learning output by using Differential Privacy (DP). The additional overhead introduced by SCALE-FL is 1% of the overhead of the plain FL executions. 

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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. An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

   https://doi.org/10.1016/j.future.2020.08.001  

   Alanezi, K.; Mishra, S. (January 2021, Future generation computer systems)

   In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energy-efficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment. 

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4. Institutional Privacy Risks in Sharing DNS Data

   https://doi.org/10.1145/3472305.3472324  

   Imana, Basileal; Korolova, Aleksandra; Heidemann, John (January 2021, Proceedings of the Applied Networking Research Workshop)

   The Domain Name System (DNS) is used in every website visit and e-mail transmission, so privacy is an obvious concern. In DNS, users ask recursive resolvers (or ``recursives'') to make queries on their behalf. Prior analysis of DNS privacy focused on privacy risks to individual end-users, mainly in traffic between users and recursives. Recursives cache and aggregate traffic for many users, factors that are commonly assumed to protect end-user privacy above the recursive. We document \emph{institutional privacy} as a new risk posed by DNS data collected at authoritative servers, even after caching and aggregation by DNS recursives. We are the first to demonstrate this risk by looking at leaks of e-mail exchanges which show communications patterns, and leaks from accessing sensitive websites, both of which can harm an institution's public image. We define a methodology to identify queries from institutions and identify leaks. We show the current practices of prefix-preserving anonymization of IP addresses and aggregation above the recursive are not sufficient to protect institutional privacy, suggesting the need for novel approaches. We demonstrate this claim by applying our methodology to real-world traffic from DNS servers that use partial prefix-preserving anonymization. Our work prompts additional privacy considerations for institutions that run their own resolvers and authoritative server operators that log and share DNS data. 

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5. Towards a Threat Model for Vehicular Fog Computing

   https://doi.org/10.1109/UEMCON47517.2019.8993064  

   Hoque, Mohammad Aminul; Hasan, Ragib (October 2019, IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON))

   null (Ed.)

   Security is a huge challenge in vehicular networks due to the large size of the network, high mobility of nodes, and continuous change of network topology. These challenges are also applicable to the vehicular fog, which is a new computing paradigm in the context of vehicular networks. In vehicular fog computing, the vehicles serve as fog nodes. This is a promising model for latency-sensitive and location-aware services, which also incurs some unique security and privacy issues. However, there is a lack of a systematic approach to design security solutions of the vehicular fog using a comprehensive threat model. Threat modeling is a step-by-step process to analyze, identify, and prioritize all the potential threats and vulnerabilities of a system and solve them with known security solutions. A well-designed threat model can help to understand the security and privacy threats, vulnerabilities, requirements, and challenges along with the attacker model, the attack motives, and attacker capabilities. Threat model analysis in vehicular fog computing is critical because only brainstorming and threat models of other vehicular network paradigms will not provide a complete scenario of potential threats and vulnerabilities. In this paper, we have explored the threat model of vehicular fog computing and identified the threats and vulnerabilities using STRIDE and CIAA threat modeling processes. We posit that this initiative will help to improve the security and privacy system design of vehicular fog computing. 

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