Latest Privacy-Preserving ML Research Papers
The newest Privacy-Preserving ML papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Privacy-Preserving ML so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
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- From “Be Kind, Rewind” to the 21st-Century Data Mine: The Video Privacy Protection ActJenna Kornicki · Seton Hall University eRepo... · Jan 1, 2027
- A House of Cards: Humphrey’s Executor, Trump v. Slaughter, Latombe, and the Structural Vulnerability of the EU-US Data Privacy FrameworkNoah Jaffe · Seton Hall University eRepo... · Jan 1, 2027
After the Privacy Shield dissolved, the Commission entered into talks with the US to adopt a new adequacy decision that would meet essential equivalency and the CJEU standard under GDPR Article 45. 10 The US adopted Executive Order 14,086 (…
- Beyond GDPR: The Architectural Challenge of Data Sovereignty and Confidential Computing in the Post-2024 EraMr. Tayabur Rahman Laskar · Zenodo (CERN European Organ... · Dec 18, 2026
As organizations migrate legacy datasets to cloud-native architectures, the tension between Big Data analytics and data privacy regulations has reached a critical inflection point. With the full operationalization of India’s Digital Persona…
- Beyond GDPR: The Architectural Challenge of Data Sovereignty and Confidential Computing in the Post-2024 EraMr. Tayabur Rahman Laskar · Zenodo (CERN European Organ... · Dec 18, 2026
As organizations migrate legacy datasets to cloud-native architectures, the tension between Big Data analytics and data privacy regulations has reached a critical inflection point. With the full operationalization of India’s Digital Persona…
- Deep Learning for Human Activity Recognition: A Comprehensive Review of Architectures, Performance, and Challenges Across Five Sensory DatasetsAbeer FathAllah Brery, Ascensión Gallardo-Antolín, Mahmoud Fakhry, Israel Gonzalez-Carrasco · Zenodo (CERN European Organ... · Aug 15, 2026
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collec…
- Deep Learning for Human Activity Recognition: A Comprehensive Review of Architectures, Performance, and Challenges Across Five Sensory DatasetsAbeer FathAllah Brery, Ascensión Gallardo-Antolín, Mahmoud Fakhry, Israel Gonzalez-Carrasco · Zenodo (CERN European Organ... · Aug 15, 2026
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collec…
- A GOVERNANCE-ORIENTED FRAMEWORK FOR BALANCING EXPLAINABILITY AND PRIVACY IN AI-BASED DECISION SYSTEMSAndressa Girotto Vargas, Edna Dias Canedo · Journal of the Association ... · Aug 15, 2026
The use of AI-based decision systems in high-impact domains highlights the need to balance explainability and privacy. While explainable AI (XAI) promotes transparency and accountability, privacy-preserving techniques restrict information d…
- Landseer: Exploring the Machine Learning Defense LandscapeAyushi Sharma, Rosemary Agbozo, Santiago Torres-Arias, Zahra Ghodsi · arXiv · May 26, 2026
Machine learning systems face diverse threats that undermine robustness, privacy, and fairness. Although many defenses have been proposed, each typically addresses a single risk in isolation. Real-world deployments, however, require these d…
- Practical Anonymous Two-Party Gradient Boosting Decision TreeHuang Chenyu, Zhang Fan, Du Minxin, Chow Sherman SM et al. · arXiv · May 26, 2026
Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High speed and interpretability make GBDTs popular in finance and h…
- Privacy-Preserving Screening for Record LinkageChenyu Huang, Fan Zhang, Huangxun Chen, Yongjun Zhao et al. · arXiv · May 26, 2026
In an era dominated by big data and machine learning, establishing valuable data collaboration has never been more critical. However, such collaborations must operate under regulatory and legal constraints. Two-party Privacy-Preserving Reco…
- The Personalization Paradox in AI-Driven Tourism E-Commerce: Psychological Reactance, Threat-Substitution, and the Moderating Role of Privacy ConcernsHongmei Duan, Ahmad Yahya Dawod, Guochao Wan · Journal of theoretical and ... · Apr 21, 2026
AI-driven personalization (AIP) has become a core mechanism of digital commerce platforms, yet its psychological consequences remain theoretically fragmented. Drawing on the Stimulus–Organism–Response (SOR) framework and Psychological React…
- Public use of a generalist LLM chatbot for health queriesBeatriz Costa-Gomes, Pavel Tolmachev, Eloise Taysom, Viknesh Sounderajah et al. · Nature Health · Apr 16, 2026
Abstract Here we analyse over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational artificial intelligence (AI) about health. We apply a hierarchical inte…
- Designing Privacy-Preserving Financial Risk Analytics on Solid PodsOshani Seneviratne, Fernando Spadea, Lorenzo Carta · SoSy2026-Privacy Paper · Mar 9, 2026
We propose a decentralized, privacy-first architecture for predicting consumer financial distress, evolving beyond simulated federated environments toward a deployable, user-centric design. Leveraging Solid pods, we enforce structural data …
- FracFace: Breaking the Visual Clues—Fractal-Based Privacy-Preserving Face RecognitionWanying Dai, Beibei Li, Naipeng Dong, Guangdong Bai et al. · NeurIPS 2025 poster · Sep 18, 2025
Face recognition is essential for identity authentication, but the rich visual clues in facial images pose significant privacy risks, highlighting the critical importance of privacy-preserving solutions. For instance, numerous studies have …
- Privacy-Preserving Hyperparameter Tuning for Federated Learning[object Object], [object Object], [object Object] · IEEE Transactions on Privacy · Jan 1, 2025
In this paper, we study the open problem of privacy-preserving hyperparameter (HP) tuning for cross-silo federated learning (FL). We first perform a comprehensive measurement study and benchmark various single-shot HP tuning strategies comp…
- Model Entanglement for solving Privacy Preserving in Federated LearningTaiyu Wang, Kaiming Zhu, Junbo Wang, Haodong Chen et al. · Submitted to ICLR 2025 · Sep 28, 2024
Federated learning (FL) is widely adopted as a secure and reliable distributed machine learning system for it allows participants to retain their training data locally, transmitting only model updates, such as gradients or parameters. Howev…
- Privacy-Preserving Machine Learning [Cryptography]Florian Kerschbaum, Nils Lukas · IEEE Security & Privacy · Nov 1, 2023
Privacy challenges in machine learning can stem from leakage by the model or from distributed data sources. Differential privacy addresses model leakage and computation over encrypted data the other. During training cryptographic approaches…
- Privacy-Preserving Occupancy Estimation[object Object], [object Object], [object Object] · IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) · May 5, 2023
In this paper, we introduce an audio-based framework for occupancy estimation, including a new public dataset, and evaluate occupancy in a ‘cocktail party’ scenario where the party is simulated by mixing audio to produce speech with overlap…
- Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and ChallengesMehdi Akbari Gurabi, Felix Hermsen, Avikarsha Mandal, Stefan Decker · Privacy and Identity Management 2023 · Jan 1, 2023
The world of big data has unlocked novel avenues for organizations to generate value via sharing data. Current data ecosystem initiatives such as Gaia-X and IDS are introducing data-driven business models that facilitate access to diverse d…
- PRI: Privacy Preserving Inspection of Encrypted Network TrafficLiron Schiff, Stefan Schmid · IEEE Symposium on Security and Privacy Workshops 2016 · Jan 1, 2016
Traffic inspection is a fundamental building block of many security solutions today. For example, to prevent the leakage or exfiltration of confidential insider information, as well as to block malicious traffic from entering the network, m…
- Preserving Genome Privacy in Research StudiesShuang Wang, Xiaoqian Jiang, Dov Fox, Lucila Ohno-Machado · Medical Data Privacy Handbook 2015 · Jan 1, 2015
As the cost of genome sequencing continues to fall, whole genome sequencing data have become a viable alternative for improving diagnostic accuracy and supporting personalized medicine. Although they have the potential to advance public hea…
- Privacy-Preserving Data Mining from Outsourced DatabasesFosca Giannotti, Laks V. S. Lakshmanan, Anna Monreale, Dino Pedreschi et al. · Computers, Privacy and Data Protection 2011 · Jan 1, 2011
Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to…
- Privacy Preserving Publication of Moving Object DataFrancesco Bonchi · Privacy in Location-Based Applications 2009 · Jan 1, 2009
The increasing availability of space-time trajectories left by location-aware devices is expected to enable novel classes of applications where the discovery of consumable, concise, and actionable knowledge is the key step. However, the ana…
- A Survey of Quantification of Privacy Preserving Data Mining AlgorithmsElisa Bertino, Dan Lin, Wei Jiang · Privacy-Preserving Data Mining 2008 · Jan 1, 2008
The aim of privacy preserving data mining (PPDM) algorithms is to extract relevant knowledge from large amounts of data while protecting at the same time sensitive information. An important aspect in the design of such algorithms is the ide…
- A Survey of Privacy-Preserving Methods Across Vertically Partitioned DataJaideep Vaidya · Privacy-Preserving Data Mining 2008 · Jan 1, 2008
The goal of data mining is to extract or “mine” knowledge from large amounts of data. However, data is often collected by several different sites. Privacy, legal and commercial concerns restrict centralized access to this data, thus deraili…
- Measures of AnonymitySuresh Venkatasubramanian · Privacy-Preserving Data Mining 2008 · Jan 1, 2008
To design a privacy-preserving data publishing system, we must first quantify the very notion of privacy, or information loss. In the past few years, there has been a proliferation of measures of privacy, some based on statistical considera…
- Privacy-Preserving Data Set Union[object Object], [object Object], [object Object], [object Object] · Privacy in Statistical Databases 2006 · Dec 31, 2006
This paper describes a cryptographic protocol for merging two or more data sets without divulging those identifying records; technically, the protocol computes a blind set-theoretic union. Applications for this protocol arise, for example, …