Towards Usable Privacy Management for IoT TAPs: Deriving Privacy Clusters and Preference Profiles

• URL: http://arxiv.org/abs/2511.11209v1
• Date: Fri, 14 Nov 2025 12:08:58 GMT
• Title: Towards Usable Privacy Management for IoT TAPs: Deriving Privacy Clusters and Preference Profiles
• Authors: Piero Romare, Farzaneh Karegar, Simone Fischer-Hübner,
• Abstract summary: IoT Trigger-Action Platforms (TAPs) typically offer coarse-grained permission controls.<n>This paper contributes to usable privacy management for TAPs by deriving privacy clusters and profiles for different types of users.
• Score: 1.2744523252873352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: IoT Trigger-Action Platforms (TAPs) typically offer coarse-grained permission controls. Even when fine-grained controls are available, users are likely overwhelmed by the complexity of setting privacy preferences. This paper contributes to usable privacy management for TAPs by deriving privacy clusters and profiles for different types of users that can be semi-automatically assigned or suggested to them. We developed and validated a questionnaire, based on users' privacy concerns regarding confidentiality and control and their requirements towards transparency in TAPs. In an online study (N=301), where participants were informed about potential privacy risks, we clustered users by their privacy concerns and requirements into Basic, Medium and High Privacy clusters. These clusters were then characterized by the users' data sharing preferences, based on a factorial vignette approach, considering the data categories, the data recipient types, and the purpose of data sharing. Our findings show three distinct privacy profiles, providing a foundation for more usable privacy controls in TAPs.

Related papers

• Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences [73.5779077857545]
  We build a framework where a local model uses these instructions to rewrite queries, only hiding details deemed sensitive by the user, before sending them to an external model.<n>Experiments with lightweight local LLMs show that, after fine-tuning, they markedly exceed the performance of much larger zero-shot models.<n>At the same time, the system still faces challenges in fully adhering to user instructions, underscoring the need for models with a better understanding of user-defined privacy preferences.
  arXiv  Detail & Related papers  (2025-07-07T18:22:55Z)
• Privacy Bills of Materials: A Transparent Privacy Information Inventory for Collaborative Privacy Notice Generation in Mobile App Development [23.41168782020005]
  We introduce PriBOM, a systematic software engineering approach to better capture and coordinate mobile app privacy information.<n>PriBOM facilitates transparency-centric privacy documentation and specific privacy notice creation, enabling traceability and trackability of privacy practices.
  arXiv  Detail & Related papers  (2025-01-02T08:14:52Z)
• On the Differential Privacy and Interactivity of Privacy Sandbox Reports [78.85958224681858]
  The Privacy Sandbox initiative from Google includes APIs for enabling privacy-preserving advertising functionalities.<n>We provide an abstract model for analyzing the privacy of these APIs and show that they satisfy a formal DP guarantee.
  arXiv  Detail & Related papers  (2024-12-22T08:22:57Z)
• Inference Privacy: Properties and Mechanisms [8.471466670802817]
  Inference Privacy (IP) can allow a user to interact with a model while providing a rigorous privacy guarantee for the users' data at inference.<n>We present two types of mechanisms for achieving IP: namely, input perturbations and output perturbations which are customizable by the users.
  arXiv  Detail & Related papers  (2024-11-27T20:47:28Z)
• PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
  PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories.<n>We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds.<n>State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
  arXiv  Detail & Related papers  (2024-08-29T17:58:38Z)
• Privacy-Preserving Data Management using Blockchains [0.0]
  Data providers need to control and update existing privacy preferences due to changing data usage. This paper proposes a blockchain-based methodology for preserving data providers private and sensitive data.
  arXiv  Detail & Related papers  (2024-08-21T01:10:39Z)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• Tapping into Privacy: A Study of User Preferences and Concerns on Trigger-Action Platforms [0.0]
  The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT) TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions.
  arXiv  Detail & Related papers  (2023-08-11T14:25:01Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• Private Reinforcement Learning with PAC and Regret Guarantees [69.4202374491817]
  We design privacy preserving exploration policies for episodic reinforcement learning (RL) We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP) We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee.
  arXiv  Detail & Related papers  (2020-09-18T20:18:35Z)
• Practical Privacy Preserving POI Recommendation [26.096197310800328]
  We propose a novel Privacy preserving POI Recommendation (PriRec) framework. PriRec keeps users' private raw data and models in users' own hands, and protects user privacy to a large extent. We apply PriRec in real-world datasets, and comprehensive experiments demonstrate that, compared with FM, PriRec achieves comparable or even better recommendation accuracy.
  arXiv  Detail & Related papers  (2020-03-05T06:06:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.