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By Paul McKellips. 2014

140 citations recorded by the source corpus.

Subfields: Education, General Health Professions, Visual Arts and Performing Arts

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Citations in the corpus

Exploiting Unintended Feature Leakage in Collaborative Learning (2018, background): “Membership inference attacks against aggregate statistics are well-known [14, 27, 50], and recent work demonstrated black-box membership inference against ML models [24, 34, 53, 58], as discussed in Section 10.”
Auditing Data Provenance in Text-Generation Models (2018, background): “Membership inference attacks against aggregate statistics have been demonstrated in the context of genomic studies [13], location time-series [26], and noisy statistics in general [8].”
LOGAN: Membership Inference Attacks Against Generative Models (2017, background): “Additional membership inference attacks focus on genomic research studies [5, 24], whereby an attacker aims to infer the presence of a particular individual’s data within an aggregate genomic dataset, or aggregate locations [50].”
Inference Attacks Against Collaborative Learning (2018, background): “, in the context of genomic studies [4, 25], location timeseries [42], or noisy statistics in general [14].”
VIP: Finding important people in images (2015, background): “[24] contains face track annotations (with character identification) for the first six episodes of the ‘Big Bang Theory’ TV series.”
Feeling Authentic on Social Media: Subjective Authenticity Across Instagram Stories and Posts (2021, background): “Moreover, in Stories, the “imagined audience” (Litt, 2012) becomes much more concrete as users are informed about their actual audience through their Story views.”
Quantifying the invisible audience in social networks (2013, background): “Our notion of the invisible audience is tied to the imagined audience in social media [27, 26].”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “However, when it comes to broadcasting or highly public spaces, receivers are often “imagined” by the speaker (Litt, 2012) and are po-”
“Participant” Perceptions of Twitter Research Ethics (2018, background): “Misalignment of imagined and actual audience can be dangerous when there are more eyes to catch a potential faux pas (Litt, 2012).”
Preserving Data Privacy via Federated Learning: Challenges and Solutions (2020, background): “Irrespective of the promising collaboration via federated learning, some attacks [7] have demonstrated that machine learning models remembered”