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1. Ask the Experts: What Should Be on an IoT Privacy and Security Label?

   https://doi.org/10.1109/SP40000.2020.00043  

   Emami-Naeini, Pardis; Agarwal, Yuvraj; Faith Cranor, Lorrie; Hibshi, Hanan (May 2020, The 41st IEEE Symposium on Security and Privacy)

   Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers—a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts’ rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoTrelated purchase decisions. 

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2. The Role of Product Reviewers in Evaluating Security and Privacy

   Guo, Wentao; Walter, Jason; Mazurek, Michelle L. (May 2022, 6th IEEE SPW Workshop on Technology and Consumer Protection (ConPro ’22))

   As consumers adopt new Internet-connected devices, apps, and other software, they are often exposed to security and privacy vulnerabilities that they likely do not have time, exper- tise, or incentive to evaluate themselves. Can professionals and institutions help by evaluating the security and privacy of these products on behalf of consumers? As a first step, we interview product reviewers about their work, specifically whether and how they incorporate security and privacy. To inform our interview design, we conduct content analysis on published product reviews to identify security- or privacy-relevant content. 

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3. The Role of Professional Product Reviewers in Evaluating Security and Privacy

   Guo, Wentao; Walter, Jason; Mazurek, Michelle L (August 2023, USENIX)

   Consumers who use Internet-connected products are often exposed to security and privacy vulnerabilities that they lack time or expertise to evaluate themselves. Can professional product reviewers help by evaluating security and privacy on their behalf? We conducted 17 interviews with product reviewers about their procedures, incentives, and assumptions regarding security and privacy. We find that reviewers have some incentives to evaluate security and privacy, but they also face substantial disincentives and challenges, leading them to consider a limited set of relevant criteria and threat models. We recommend future work to help product reviewers provide useful advice to consumers in ways that align with reviewers' business models and incentives. These include developing usable resources and tools, as well as validating the heuristics they use to judge security and privacy expediently. 

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4. Knowledge Infusion in Privacy Preserving Data Generation

   Kotal, Anantaa; Das, Nilanjana; Joshi, Anupam (August 2023, KDD Workshop on Knowledge-infused Learning, 29TH ACM SIGKDD)

   Security monitoring is crucial for maintaining a strong IT infrastructure by protecting against emerging threats, identifying vulnerabilities, and detecting potential points of failure. It involves deploying advanced tools to continuously monitor networks, systems, and configurations. However, organizations face challenges in adapting modern techniques like Machine Learning (ML) due to privacy and security risks associated with sharing internal data. Compliance with regulations like GDPR further complicates data sharing. To promote external knowledge sharing, a secure and privacy-preserving method for organizations to share data is necessary. Privacy-preserving data generation involves creating new data that maintains privacy while preserving key characteristics and properties of the original data so that it is still useful in creating downstream models of attacks. Generative models, such as Generative Adversarial Networks (GAN), have been proposed as a solution for privacy preserving synthetic data generation. However, standard GANs are limited in their capabilities to generate realistic system data. System data have inherent constraints, e.g., the list of legitimate I.P. addresses and port numbers are limited, and protocols dictate a valid sequence of network events. Standard generative models do not account for such constraints and do not utilize domain knowledge in their generation process. Additionally, they are limited by the attribute values present in the training data. This poses a major privacy risk, as sensitive discrete attribute values are repeated by GANs. To address these limitations, we propose a novel model for Knowledge Infused Privacy Preserving Data Generation. A privacy preserving Generative Adversarial Network (GAN) is trained on system data for generating synthetic datasets that can replace original data for downstream tasks while protecting sensitive data. Knowledge from domain-specific knowledge graphs is used to guide the data generation process, check for the validity of generated values, and enrich the dataset by diversifying the values of attributes. We specifically demonstrate this model by synthesizing network data captured by the network capture tool, Wireshark. We establish that the synthetic dataset holds up to the constraints of the network-specific datasets and can replace the original dataset in downstream tasks. 

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5. User Privacy Protection via Windows Registry Hooking and Runtime Encryption

   https://doi.org/10.3390/s24165106  

   Amoruso, Edward L; Leinecker, Richard; Zou, Cliff C (August 2024, Sensors)

   The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are unencrypted and thus vulnerable to exploitation by malicious actors who gain access to this repository. To address this security and privacy concern, we propose a novel approach that efficiently encrypts and decrypts sensitive registry data in real time. Our developed proof-of-concept program intercepts interactions between the registry’s application programming interfaces (APIs) and other Windows applications using an advanced hooking technique. This enables the proposed system to be transparent to users without requiring any changes to the operating system or installed software. Our approach also implements the data protection API (DPAPI) developed by Microsoft to securely manage each user’s encryption key. Ultimately, our research provides an enhanced security and privacy framework for the Windows registry, effectively fortifying the registry against security and privacy threats while maintaining its accessibility to legitimate users and applications. 

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