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Works (7)
Updated: April 5th, 2024 11:48
2023 article
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning
PROCEEDINGS 8TH ACM/IEEE CONFERENCE ON INTERNET OF THINGS DESIGN AND IMPLEMENTATION, IOTDI 2023, pp. 288–300.
author keywords: Multi-task learning; neural networks; collaborative intelligence; differential privacy; task privacy
TL;DR:
A novel deep learning-based privacy-cognizant feature generation process called “MetaMorphosis” is proposed that outperforms recent adversarial learning and universal feature generation methods by guaranteeing privacy requirements in an efficient way for image and video analytics.
(via Semantic Scholar)
open access via arxiv.org (repository)
Source: Web Of Science
Added: January 16, 2024
2023 article
Speaker Orientation-Aware Privacy Control to Thwart Misactivation of Voice Assistants
2023 53RD ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS, DSN, pp. 597–610.
author keywords: Voice assistant; Privacy control; Signal processing
TL;DR:
This paper proposes a device-free, non-obtrusive acoustic sensing system called HeadTalk, which leverages the user's head direction information and verifies that a human generates the sound to minimize accidental activations of VAs.
(via Semantic Scholar)
Source: Web Of Science
Added: September 11, 2023
2023 article
VoicePM: A Robust Privacy Measurement on Voice Anonymity
PROCEEDINGS OF THE 16TH ACM CONFERENCE ON SECURITY AND PRIVACY IN WIRELESS AND MOBILE NETWORKS, WISEC 2023, pp. 215–226.
author keywords: Voice assistant; Voice anonymity; Privacy control
TL;DR:
This study develops a tradeoff metric to capture voice biometrics as well as different voice attributes and proposes VoicePM, a robust Voice Privacy Measurement framework, to study the feasibility of applying different state-of-the-art voice anonymization solutions to achieve the optimum tradeoff between privacy and utility.
(via Semantic Scholar)
Source: Web Of Science
Added: August 21, 2023
2022 article
Hey Alexa, Who Am I Talking to?: Analyzing Users' Perception and Awareness Regarding Third-party Alexa Skills
PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22).
author keywords: Voice assistant; Third-party skills; Security indicators
TL;DR:
An interactive user study where participants listen to and interact with real-world skills using the official Alexa app finds that most participants fail to identify the skill developer correctly and cannot correctly determine which skills will be automatically activated through the voice interface.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: May 1, 2023
2021 article
Hey Alexa, is this Skill Safe?: Taking a Closer Look at the Alexa Skill Ecosystem
28TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2021).
TL;DR:
The first large-scale analysis of Alexa skills is performed, obtained from seven different skill stores totaling to 90,194 unique skills, revealing several limitations that exist in the current skill vetting process and providing some suggestions for strengthening the overall ecosystem and thereby enhance transparency for end-users.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: August 30, 2021
2021 article
Understanding People's Attitude and Concerns towards Adopting IoT Devices
EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21).
author keywords: Internet of Things (IoT); user attitude; cross-societal concerns
TL;DR:
Recommendations are provided to reduce users’ concerns in adopting IoT devices, and thereby enhance user trust towards adopting Internet of Things devices.
(via Semantic Scholar)
Source: Web Of Science
Added: March 28, 2022
2018 article
The Web's Sixth Sense: A Study of Scripts Accessing Smartphone Sensors
PROCEEDINGS OF THE 2018 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'18), pp. 1515–1532.
author keywords: Sensors; Mobile browser; On-line tracking; Fingerprinting
TL;DR:
It is found that popular tracking protection lists such as EasyList and Disconnect commonly fail to block most tracking scripts that misuse sensors and even privacy-focused browsers fail to implement mitigations suggested by W3C, which includes limiting sensor access from insecure contexts and cross-origin iframes.
(via Semantic Scholar)
Source: Web Of Science
Added: April 9, 2019
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