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Towards the vision of building artificial intelligence systems that can assist with our everyday life, we introduce a proof of concept for a social media privacy "cyborg" which can locally and privately monitor a person's published content and offer advice or warnings when their privacy is at stake. The idea of a cyborg can be more general, as a separate local entity with its own computational resources, that can automatically perform several online tasks on our behalf. For this demonstration, we assume an attacker that can successfully infer user attributes, solely based on what the user has published (topic-based inference). We focus on Social Media privacy and specifically on the issue of exposing sensitive user-attributes, like location, or race, through published content. We built a privacy cyborg that can monitor a user's posted topics and automatically warn them in real time when a sensitive attribute is at risk of being exposed.
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Review and synthesis of expert perspectives on user attribute and profile definitions for fashion recommendation
A key obstacle in personalised fashion recommendations is the challenge of capturing user physical attributes at a large scale, which limits exclusively computational methods (like machine learning) to readily available attributes whose influence on recommendation accuracy is variable. Expert advice is a potential means of identifying influential user attributes. However, individual experts often disagree or offer conflicting advice. Thus, identifying areas where expert advice is or isn’t consistent, in the context of user attributes and profiling is critical. Here, we characterise the breadth of expert definitions of user attributes and profiles through an exhaustive assessment of 156 years of advice literature. Expert definitions of body colouring, shape, and personality attributes are extracted and compared. The range of attribute-value relationships and profile definitions in each domain is described, and coherence among authors for each domain is discussed.
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- Award ID(s):
- 1715200
- PAR ID:
- 10540030
- Publisher / Repository:
- Taylor & Francis
- Date Published:
- Journal Name:
- International Journal of Fashion Design, Technology and Education
- Volume:
- 17
- Issue:
- 2
- ISSN:
- 1754-3266
- Page Range / eLocation ID:
- 202 to 213
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/17543266.2023.2261228
