From Framework to Practice: Youth Negotiations of Privacy with Smart Voice Assistants Through the PEA-AI Lens
- URL: http://arxiv.org/abs/2603.00081v1
- Date: Sat, 14 Feb 2026 03:03:57 GMT
- Title: From Framework to Practice: Youth Negotiations of Privacy with Smart Voice Assistants Through the PEA-AI Lens
- Abstract summary: This study applies the Privacy-Ethics Alignment in AI framework to examine how youth perceive and negotiate privacy within SVAs.<n>We measured five privacy constructs: Perceived Privacy Risk (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy-Protective Behavior (PPB)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Smart voice assistants (SVAs) have become embedded in the daily lives of youth, introducing complex privacy challenges due to always-on listening, shared device usage, and opaque data practices. This study applies the Privacy-Ethics Alignment in AI (PEA-AI) framework to examine how youth perceive and negotiate privacy within SVAs. Through a survey of 469 Canadian youth (aged 16-24), we measured five privacy constructs: Perceived Privacy Risk (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy-Protective Behavior (PPB). Results reveal a persistent privacy paradox. While youth express moderate to high privacy concerns (PPR M = 3.61), perceived benefits (PPBf M = 3.00) and protective actions (PPB M = 3.03) remain moderate, with transparency and trust scoring lowest (ATT M = 2.52). Heavy SVA users report higher benefits and lower risk perception than light users. High protective behavior is strongly associated with both high risk perception and high self-efficacy. Qualitative insights from prior focus groups contextualize these patterns, illustrating how youth navigate tensions between convenience, control, and trust. The findings provide actionable design principles for SVA along with implications for multi-stakeholder governance and youth-centered digital literacy.
Related papers
- Negotiating Privacy with Smart Voice Assistants: Risk-Benefit and Control-Acceptance Tensions [0.0]
This study introduces a negotiation-based framework for understanding youth privacy decision-making with SVAs.<n>We examine the distribution of two composite indices: the Risk-Benefit Tension Index (RBTI) and the Control-Acceptance Tension Index (CATI) using survey data from 469 Canadian youth aged 16-24.
arXiv Detail & Related papers (2026-04-04T06:35:59Z) - Contextualized Privacy Defense for LLM Agents [84.30907378390512]
LLM agents increasingly act on users' personal information, yet existing privacy defenses remain limited in both design and adaptability.<n>We propose Contextualized Defense Instructing (CDI), a new privacy defense paradigm.<n>We show that our CDI consistently achieves a better balance between privacy preservation (94.2%) and helpfulness (80.6%) than baselines.
arXiv Detail & Related papers (2026-03-03T13:35:33Z) - Privacy by Voice: Modeling Youth Privacy-Protective Behavior in Smart Voice Assistants [0.0]
This study investigates how Canadian youth negotiate privacy with Smart Voice Assistants (SVAs)<n>It develops and tests a structural model grounded in five key constructs: perceived privacy risks (PPR), perceived benefits (PPBf), algorithmic transparency and trust (ATT), privacy self-efficacy (PSE) and privacy-protective behaviors (PPB)
arXiv Detail & Related papers (2026-02-09T05:56:51Z) - Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy [50.66105844449181]
Individual Differential Privacy (iDP) promises users control over their privacy, but this promise can be broken in practice.<n>We reveal a previously overlooked vulnerability in sampling-based iDP mechanisms.<n>We propose $(varepsilon_i,_i,overline)$-iDP a privacy contract that uses $$-divergences to provide users with a hard upper bound on their excess vulnerability.
arXiv Detail & Related papers (2026-01-19T10:26:12Z) - Balancing Usability and Compliance in AI Smart Devices: A Privacy-by-Design Audit of Google Home, Alexa, and Siri [0.0]
This paper investigates the privacy and usability of AI-enabled smart devices commonly used by youth.<n>It focuses on Google Home Mini, Amazon Alexa, and Apple Siri.
arXiv Detail & Related papers (2026-01-07T21:20:58Z) - Convenience vs. Control: A Qualitative Study of Youth Privacy with Smart Voice Assistants [0.0]
We investigate how perceived privacy risks (PPR) and benefits (PPBf) intersect with algorithmic transparency and trust (ATT) and privacy self-language overload (PSE)<n>Our analysis reveals that policy, fragmented settings, and unclear data retention undermine self-efficacy and discourage protective actions.<n>We derive actionable design guidance for SVAs, including a unified privacy hub, plain-language "data nutrition" labels, clear retention defaults, and device-conditional micro-tutorials.
arXiv Detail & Related papers (2026-01-07T21:15:29Z) - Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attacks [13.326888254423901]
VLM-based attribute inference attacks have emerged as a serious privacy concern, enabling adversaries to infer private attributes from images shared on social media.<n>We propose a novel protection method that jointly optimize privacy suppression and utility preservation under a visual consistency constraint.<n>Our method effectively reduces PAR below 25%, keeps NPAR above 88%, and generalizes well to unseen and paraphrased privacy questions.
arXiv Detail & Related papers (2025-12-20T08:08:50Z) - MAGPIE: A benchmark for Multi-AGent contextual PrIvacy Evaluation [61.92403071137653]
Existing privacy benchmarks only focus on simplistic, single-turn interactions where private information can be trivially omitted without affecting task outcomes.<n>We introduce MAGPIE, a novel benchmark designed to evaluate privacy understanding and preservation in multi-agent collaborative, non-adversarial scenarios.<n>Our evaluation reveals that state-of-the-art agents, including GPT-5 and Gemini 2.5-Pro, exhibit significant privacy leakage.
arXiv Detail & Related papers (2025-10-16T23:12:12Z) - Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent [3.89966779727297]
Language model (LM) agents that act on users' behalf for personal tasks can boost productivity, but are also susceptible to unintended privacy leakage risks.<n>We present the first study on people's capacity to oversee the privacy implications of the LM agents.
arXiv Detail & Related papers (2024-11-02T19:15:42Z) - 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) - Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory [82.7042006247124]
We show that even the most capable AI models reveal private information in contexts that humans would not, 39% and 57% of the time, respectively.
Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
arXiv Detail & Related papers (2023-10-27T04:15:30Z) - 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)
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.