Privacy-Utility Tradeoff

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Position: Embodied AI Requires a Privacy-Utility Trade-off
May 6, 2026Xiaoliang Fan, Jiarui Chen, Zhuodong Liu +6Privacy-Preserving ML Embodied AI
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University, China Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China · School of Computing and Information Systems, The University of Melbourne, Australia
Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
May 6, 2026Xinyan Han, Yan Lu, Xiaoyu Lin +5Synthetic Data Augmentation Privacy-Preserving ML
1Tsinghua University · 2Southeast University · 3Siemens China
Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM
May 5, 2026Tianyu Wang, Luhao Zhang, Rachel CummingsDifferential Privacy Privacy-Utility Tradeoff
Department of Industrial Engineering and Operations Research, Columbia University. · Department of Applied Mathematics and Statistics, Johns Hopkins University.
Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability
May 3, 2026Yexin Zhang, Zhongtian Ma, Qiaosheng Zhang +1Algorithmic Stability Privacy-Preserving ML
School of Cybersecurity, Northwestern Polytechnical University, Xi’an, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Shanghai Innovation Institute, Shanghai, China
Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption
May 1, 2026Gaoyi Chen, Minghao Li, Weishi Shi +4Data Leakage Privacy Auditing
Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA · Department of Electrical Engineering, University of North Texas, Denton, TX, USA
A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
May 1, 2026Stephen Meisenbacher, Angelo Kleinert, Florian MatthesDocument Chunking Differential Privacy
Technical University of Munich School of Computation, Information and Technology Department of Computer Science Garching, Germany
Shuffling-Aware Optimization for Private Vector Mean Estimation
Apr 30, 2026Shun Takagi, Seng Pei LiewDifferential Privacy Privacy-Utility Tradeoff
LY Corporation, Tokyo, Japan
Differentially-Private Text Rewriting reshapes Linguistic Style
Apr 29, 2026Stefan ArnoldPrivacy-Preserving Language Models Differential Privacy
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
Apr 29, 2026Peter Müllner, Dominik Kowald, Markus Schedl +1Differential Privacy Recommender Systems
Know Center Research GmbH · Graz, Austria · Know Center Research GmbH & University of Graz +3
When Agents Shop for You: Role Coherence in AI-Mediated Markets
Apr 29, 2026Soogand Alavi, Salar NozariPrivacy Leakage in Language Models Agentic Commerce
Tippie College of Business, University of Iowa
X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange
Apr 27, 2026Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1Automated Negotiation Differential Privacy
Department of Informatics, University of Oslo, Norway · *University of Electronic Science and Technology of China
Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation
Apr 23, 2026Michele Miranda, Xinlan Yan, Nishant Mishra +4Healthcare Differential Privacy
Sapienza University of Rome · Amsterdam UMC · University of Amsterdam +1
A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities
Apr 21, 2026Aya Cherigui, Florent Guépin, Arnaud Legendre +1Membership Inference Attacks Synthetic Data Generation
Orange Research, Belfort, France · Université Marie et Louis Pasteur, CNRS, institut FEMTO-ST (UMR 6174), F-25000 Besançon, France
A Case Study on the Impact of Anonymization Along the RAG Pipeline
Apr 17, 2026Andreea-Elena Bodea, Stephen Meisenbacher, Florian MatthesRetrieval-Augmented Generation RAG Security
Technical University of Munich · School of Computation, Information and Technology · Garching, Germany
PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems
Feb 21, 2026Nnaemeka Obiefuna, Samuel Oyeneye, Similoluwa Odunaiya +2Privacy-Preserving ML Differential Privacy
1ML Collective · 2Friedrich-Alexander-Universität Erlangen-Nürnberg · 3Carnegie Mellon University
Efficient privacy loss accounting for subsampling and random allocation
Feb 19, 2026Vitaly Feldman, Moshe ShenfeldDifferential Privacy Privacy-Utility Tradeoff
Apple · The Hebrew University of Jerusalem · *Work partially done while author was an intern at Apple.
Protect Your Score: Contact Tracing With Differential Privacy Guarantees
Date pendingRob Romijnders, Christos Louizos, Yuki M. Asano +1Differential Privacy Privacy-Utility Tradeoff
University of Amsterdam · Qualcomm AI research
PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
Date pendingMojtaba Taherisadr, Salma ElmalakiRL Control Cyber-Physical Systems
University of California, Irvine, USA
Demystifying the Privacy-Utility Trade-off in LLM Interactions
Date pendingZhenhua Liu, Zhanxu Xie, Junjie Yu +3Privacy-Preserving Language Models Privacy Leakage in Language Models
Soochow University · Beihang University · Suzhou City University +2

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Position: Embodied AI Requires a Privacy-Utility Trade-off

Xiaoliang Fan, Jiarui Chen, Zhuodong Liu, Ziqi Yang+5

Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University, China Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China · School of Computing and Information Systems, The University of Melbourne, Australia

Privacy-Preserving ML Embodied AI Privacy-Utility Tradeoff

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Embodied AI (EAI) systems are rapidly transitioning from simulations into real-world domestic and other sensitive environments. However, recent EAI solutions have largely demonstrated advancements within isolated stages such as instruction, perception, planning and interaction, without considering their coupled privacy implications in high-frequency deployments where privacy leakage is often irreversible. This position paper argues that optimizing these components independently creates a systemic privacy crisis when deployed in sensitive settings, thereby advancing the position that privacy in EAI is a life cycle-level architectural constraint rather than a stage-local feature. To address these challenges, we propose Secure Privacy Integration in Next-generation Embodied AI (SPINE), a unified privacy-aware framework that treats privacy as a dynamic control signal governing cross-stage coupling throughout the entire EAI life cycle. SPINE decomposes the EAI pipeline into various stages and establishes a multi-criterion privacy classification matrix to orchestrate contextual sensitivity across stage boundaries. We conduct preliminary simulation and real-world case studies to conceptually validate how privacy constraints propagate downstream to reshape system behavior, illustrating the insufficiency of fragmented privacy patches and motivating future research directions into secure yet functional embodied AI systems. We detail the SPINE framework and case studies at https://github.com/rminshen03/EAI_Privacy_Position.

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