# Addressing Privacy Threats from Machine Learning

arXiv: https://arxiv.org/abs/2111.04439 · HTML: https://mlsanity.com/paper/2111.04439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition](https://mlsanity.com/paper/2101.07922/) (2021)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [Privacy in Deep Learning: A Survey](https://mlsanity.com/paper/2004.12254/) (2020)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)
- [Data Poisoning Won't Save You From Facial Recognition](https://mlsanity.com/paper/2106.14851/) (2021)
- [POTs: Protective Optimization Technologies](https://mlsanity.com/paper/1806.02711/) (2018)
- [Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies](https://mlsanity.com/paper/2012.09995/) (2020)
- [Face-Off: Adversarial Face Obfuscation](https://mlsanity.com/paper/2003.08861/) (2020)
- [Unpacking the Expressed Consequences of AI Research in Broader Impact Statements](https://mlsanity.com/paper/2105.04760/) (2021)
- [DeepBlur: A Simple and Effective Method for Natural Image Obfuscation](https://mlsanity.com/paper/2104.02655/) (2021)
- [Politics of Adversarial Machine Learning](https://mlsanity.com/paper/2002.05648/) (2020)
- [Oriole: Thwarting Privacy against Trustworthy Deep Learning Models](https://mlsanity.com/paper/2102.11502/) (2021)

## Built on by (most cited first, 1 of 1)

- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)

## Abstract (arXiv)

Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change, education, and more. However, many of these same researchers are expressing growing concern about applications of machine learning for surveillance (Nanayakkara et al., 2021). This paper presents a brief overview of strategies for resisting these surveillance technologies and calls for greater collaboration between machine learning and human-computer interaction researchers to address the threats that these technologies pose.