# MaSS: Multi-attribute Selective Suppression arXiv: https://arxiv.org/abs/2210.09904 ยท HTML: https://mlsanity.com/paper/2210.09904/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 36 references that are in the map. - First posted on arXiv 18 Oct 2022. ## Builds on (most cited first, 22 of 22) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020) - [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021) - [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017) - [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020) - [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019) - [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022) - [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020) - [Privacy for Free: How does Dataset Condensation Help Privacy?](https://mlsanity.com/paper/2206.00240/) (2022) - [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019) - [Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images](https://mlsanity.com/paper/1703.10660/) (2017) - [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020) - [AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark](https://mlsanity.com/paper/1807.03418/) (2018) - [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020) - [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022) - [Data Minimization for GDPR Compliance in Machine Learning Models](https://mlsanity.com/paper/2008.04113/) (2020) - [Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics](https://mlsanity.com/paper/2104.01753/) (2021) ## Built on by (most cited first, 5 of 5) - [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023) - [Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization](https://mlsanity.com/paper/2504.14301/) (2025) - [Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy](https://mlsanity.com/paper/2410.17098/) (2024) - [From Pixels to Privacy: Temporally Consistent Video Anonymization via Token Pruning for Privacy Preserving Action Recognition](https://mlsanity.com/paper/2603.26336/) (2026) - [StegaVAR: Privacy-Preserving Video Action Recognition via Steganographic Domain Analysis](https://mlsanity.com/paper/2512.12586/) (2025) ## Abstract (arXiv) The recent rapid advances in machine learning technologies largely depend on the vast richness of data available today, in terms of both the quantity and the rich content contained within. For example, biometric data such as images and voices could reveal people's attributes like age, gender, sentiment, and origin, whereas location/motion data could be used to infer people's activity levels, transportation modes, and life habits. Along with the new services and applications enabled by such technological advances, various governmental policies are put in place to regulate such data usage and protect people's privacy and rights. As a result, data owners often opt for simple data obfuscation (e.g., blur people's faces in images) or withholding data altogether, which leads to severe data quality degradation and greatly limits the data's potential utility. Aiming for a sophisticated mechanism which gives data owners fine-grained control while retaining the maximal degree of data utility, we propose Multi-attribute Selective Suppression, or MaSS, a general framework for performing precisely targeted data surgery to simultaneously suppress any selected set of attributes while preserving the rest for downstream machine learning tasks. MaSS learns a data modifier through adversarial games between two sets of networks, where one is aimed at suppressing selected attributes, and the other ensures the retention of the rest of the attributes via general contrastive loss as well as explicit classification metrics. We carried out an extensive evaluation of our proposed method using multiple datasets from different domains including facial images, voice audio, and video clips, and obtained promising results in MaSS' generalizability and capability of suppressing targeted attributes without negatively affecting the data's usability in other downstream ML tasks.