PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults

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PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults
Title:
PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults
Journal Title:
Proceedings of the ACM on Human-Computer Interaction
Publication Date:
18 October 2021
Citation:
Kandappu, T., Subbaraju, V., & Xu, Q. (2021). PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–32. doi:10.1145/3476047
Abstract:
Built-in pervasive cameras have become an integral part of mobile/wearable devices and enabled a wide range of ubiquitous applications with their ability to be “always-on”. In particular, life-logging has been identified as a means to enhance the quality of life of older adults by allowing them to reminisce about their own life experiences. However, the sensitive images captured by the cameras threaten individuals’ right to have private social lives and raise concerns about privacy and security in the physical world. This threat gets worse when image recognition technologies can link images to people, scenes, and objects, hence, implicitly and unexpectedly reveal more sensitive information such as social connections. In this paper, we first examine life-log images obtained from 54 older adults to extract (a) the artifacts or visual cues, and (b) the context of the image that influences an older life-logger’s ability to recall the life events associated with a life-log image. We call these artifacts and contextual cues “stimuli”. Using the set of stimuli extracted, we then propose a set of obfuscation strategies that naturally balances the trade-off between reminiscability and privacy (revealing social ties) while selectively obfuscating parts of the images. More specifically, our platform yields privacy-utility tradeoff by compromising, on average, modest 13.4% reminiscability scores while significantly improving privacy guarantees – around 40% error in cloud estimation.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore Ministry of Education (MOE) - Academic Research Fund (AcRF) Tier 1 grant
Grant Reference no. : 18-C220-SMU-007
Description:
© Author| ACM 2021. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the ACM on Human-Computer Interaction, https://doi.org/10.1145/3476047
ISSN:
2573-0142
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