Toward an Approach to Privacy Notices in IoT

Shayegh, Parvaneh; Ghanavati, Sepideh

doi:10.1109/rew.2017.77

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…This work is different than the work presented by Shayegh et al because an analysis of the privacy practices is provided instead of proposing a model for the analysis . This work also differs from the work of Sengul because in that work the author described privacy issues for IoT instead of analyzing privacy policies.…”

Section: Introductionmentioning

confidence: 94%

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A review and an empirical analysis of privacy policy and notices for consumer Internet of things

Pérez

Zeadally

Cochran

2018

Security and Privacy

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The privacy policies and practices of six consumer Internet of things (IoT) devices were reviewed and compared. In addition, an empirical verification of the compliance of privacy policies for data collection practices on two voice‐activated intelligent assistant devices, namely the Amazon Echo Dot and Google Home devices was performed. The review shows that IoT privacy policies may not be usable from the human‐computer interaction perspective because IoT policies are included as part of the manufacturers' general privacy policy (which may include policies unrelated to the device), or the IoT policy requires to read (in addition to the IoT policies) the manufacturers' general privacy policy which increase the cognitive load for the user. It was also found that future policy changes along with the approach to provide user consent to changes may adversely affect the privacy of the consumer because changes to policies may not provide choice to consumers to opt out from data collection practices if consumers are not aware of the changes. Finally, the empirical results for the Amazon Echo Dot and the Google Home devices demonstrate they adhere to their privacy policies when voice is collected through these devices.

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Section: Introductionmentioning

confidence: 94%

“…Similar analyses have been performed before for privacy policies for websites since the emergence of the web, and more recently for mobile applications . However, only recently works focusing on the analysis of privacy policies of IoT systems and devices have started to emerge.…”

Section: Privacy Policy and Notices For Consumer Iotmentioning

confidence: 99%

A review and an empirical analysis of privacy policy and notices for consumer Internet of things

Pérez

Zeadally

Cochran

2018

Security and Privacy

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“…Usable privacy has been studied as an approach to make privacy policies understandable using machine learning, data processing, and legal analysis [60]. Most of the related work in this area has focused on enhancing the readability and usability of privacy policies through traditional NLP techniques, user interface improvements, and deep learning [13,[60][61][62][63][64][65][66][67][68][69][70][71][72][73]. Using LLMs for usable privacy is a recent area of research.…”

Section: Privacy Policy Of Iot Devicesmentioning

confidence: 99%

Evaluating Quantized Llama 2 Models for IoT Privacy Policy Language Generation

Malisetty,

Perez

2024

Future Internet

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Quantized large language models are large language models (LLMs) optimized for model size while preserving their efficacy. They can be executed on consumer-grade computers without the powerful features of dedicated servers needed to execute regular (non-quantized) LLMs. Because of their ability to summarize, answer questions, and provide insights, LLMs are being used to analyze large texts/documents. One of these types of large texts/documents are Internet of Things (IoT) privacy policies, which are documents specifying how smart home gadgets, health-monitoring wearables, and personal voice assistants (among others) collect and manage consumer/user data on behalf of Internet companies providing services. Even though privacy policies are important, they are difficult to comprehend due to their length and how they are written, which makes them attractive for analysis using LLMs. This study evaluates how quantized LLMs are modeling the language of privacy policies to be potentially used to transform IoT privacy policies into simpler, more usable formats, thus aiding comprehension. While the long-term goal is to achieve this usable transformation, our work focuses on evaluating quantized LLM models used for IoT privacy policy language. Particularly, we study 4-bit, 5-bit, and 8-bit quantized versions of the large language model Meta AI version 2 (Llama 2) and the base Llama 2 model (zero-shot, without fine-tuning) under different metrics and prompts to determine how well these quantized versions model the language of IoT privacy policy documents by completing and generating privacy policy text.

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“…Shayegh, P. et al. 17 used the Latent Dirichlet allocation (LDA) algorithm for topic modeling to classify parts from the privacy policy documents under six topics: Information, Collection, Sharing, Permission, Purpose and Technology. In all these studies, manual or semi‐automated labeling is necessary and determining the classes/topics that the text sections of a privacy policy can be classified is a challenging task.…”

Section: Related Workmentioning

confidence: 99%

Knowledge elicitation methodology for evaluation of Internet of Things privacy characteristics in smart cities

Kilicay‐Ergin,

Barb,

Chaudhary

2023

Systems Engineering

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One of the impediments to transforming urban cities into smart cities is the security and privacy concerns that arise due to use of Internet of Things (IoT) devices in various smart city applications. While IoT device vendors publish their security and privacy policies, manual evaluation of these policies is tedious and prone to misinterpretation as there is a lot of variability in the language used across IoT vendors. Local administrations and policy analysts are faced with understanding the implications of integrating IoT devices with differing security and privacy characteristics but lack methods that support them in analysis of privacy characteristics from a holistic perspective. In this paper, a methodology for knowledge elicitation from textual information is outlined to evaluate privacy characteristics of IoT devices. The methodology includes natural language processing and deep learning techniques to evaluate the relevance of IoT privacy policies to the National Institute of Standards and Technology (NIST) security and privacy framework 5. Based on the analysis, text similarity scores are calculated for each IoT privacy policy document and each section of the policy document is labeled to NIST categories and functions. Analysis of these resulting labels and scores helps analysts to gain insights on each privacy policy as well as provide a holistic perspective of the privacy characteristics of IoT devices used in smart city applications. For example, all the policy documents used in the study talk about Protect domain and half of the documents cover Detect domain. However, most of the policies contain gaps regarding the Identify, Respond, and Recover domains. The study has implications for policy analysts, IoT vendors, and smart city administrators in terms of understanding the privacy gaps in IoT devices with respect to the NIST framework which can ultimately support policy alignment to address privacy concerns for smart cities.

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Toward an Approach to Privacy Notices in IoT

Cited by 10 publications

References 15 publications

A review and an empirical analysis of privacy policy and notices for consumer Internet of things

A review and an empirical analysis of privacy policy and notices for consumer Internet of things

Evaluating Quantized Llama 2 Models for IoT Privacy Policy Language Generation

Knowledge elicitation methodology for evaluation of Internet of Things privacy characteristics in smart cities

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