Countermeasures: Learning to Lie to Objects
- Submitting institution
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Royal College of Art(The)
- Unit of assessment
- 32 - Art and Design: History, Practice and Theory
- Output identifier
- Main1
- Type
- E - Conference contribution
- DOI
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10.1145/3290607.3310420
- Title of conference / published proceedings
- Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
- First page
- 1
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- -
- Year of publication
- 2019
- URL
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http://ispysensors.com
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- ‘Countermeasures: Learning to Lie to Objects’ presents the processes and findings of the initial phase of the Countermeasures research project, established and led by Main to investigate public awareness of the presence of sensors in digital objects, the impact of sensor-enabled objects on privacy, and methods to increase user agency over sensor-based communication. The paper was presented at the Association for Computing Machinery's 2019 Conference on Human Factors in Computing Systems (CHI) and published in the conference proceedings. Main’s Countermeasures project addresses an under-researched area of emerging significance relating to data privacy and Human Computer Interaction (HCI). Initial research undertaken by Main demonstrated that although there are established and broadly understood methods of protecting privacy online, there is little awareness of the sensors in devices such as phones, laptops, or televisions, and no established methods of disabling them for privacy purposes. The project developed and tested new practical methods of deceiving sensors, aiming to question commonly held public perceptions of communication with ‘smart’ objects. The project employed mixed-methods research across several phases. Research workshops led by Main identified existing perceptions and tested accessible methods of deceiving sensors. Comprehensive technical analysis of 15 common devices was performed to identify and classify embedded sensors, and design, prototyping, and testing of tools was carried out to create an easily reproducible toolkit. The research also resulted in a booklet , and an open source toolkit of custom-designed devices and materials to allow people to identify and deceive the sensors in household objects. These were presented to CHI conference attendees and made available to broader audiences through the website http://ispysensors.com. In 2021 Main was awarded an EPSRC Human Data Interaction Network grant to develop the concept of tool kits for lying to objects applied to the context of children’s use of technology.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
