Privacy-Utility Tradeoff

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PrivCert: Certifying Statement Support under Differential Privacy
Sep 30, 2026Tsubasa Takahashi, Takumi HiraokaDifferential Privacy Privacy-Utility Tradeoff
Acompany Co., Ltd.
Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
Sep 11, 2026Noman Sadiq, Mohsen TooraniElectroencephalography Privacy-Preserving ML
Department of Science and Industry Systems University of South-Eastern Norway Kongsberg, Norway
On the Impact of Anonymization on the Performance of Large Language Models
Sep 10, 2026Tobias Deußer, Max Hahnbück, Lorenz Sparrenberg +3LLM Evaluation Privacy-Preserving Language Models
University of Bonn, Bonn, Germany · 2Fraunhofer IAIS, Sankt Augustin, Germany · 3Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany +1
Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization
Aug 31, 2026Dishu Yang, Jingjing Liu, Jize LiLLM Alignment Memorization in Language Models
Khoury College of Computer Sciences Northeastern University San Jose, USA · Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, USA · Department of Economics Boston University Boston, USA
Performative Privacy: When Differential Privacy Maximizes Utility
Aug 28, 2026Uddalak Mukherjee, Edwige Cyffers, Yann ChevaleyreData Leakage Differential Privacy
Dauphine PSL · CNRS Dauphine PSL
Information Bottleneck under Perfect Privacy
Aug 11, 2026Junle Zhong, Mohamad Assaad, Sreejith SreekumarInformation Bottleneck Privacy-Preserving ML
Laboratoire des Signaux et Syst`emes (L2S), CNRS, CentraleSup´elec, Universit´e Paris-Saclay, 91190 Gif-sur-Yvette, France
Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs
Aug 5, 2026Sareer Ul Amin, Muhammad Ayaz, Muhammad Munsif +1Privacy-Preserving ML Privacy-Utility Tradeoff
Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea · College of Art and Technology, Chung-Ang University, Anseong, 17546, South Korea
Shapley-Value-Based Feature Attribution for Data Masking
Jul 31, 2026Xinxue, Qu, Francis Bilson Darku +1Shapley Value Attribution Privacy-Utility Tradeoff
Shawn · Department of Information Technology, Analytics, and Operations, Mendoza College of Business University of Notre Dame, South Bend, IN, U.S.A. · Department of Information Systems, W. P. Carey School of Business Arizona State University, Tempe, AZ, U.S.A.
Privacy Preserving Recommender Systems Balancing Personalization with Privacy
Jul 14, 2026Ranjeet K Jha, Venkata Suresh GummadilliDifferential Privacy Recommender Systems
College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712 USA
Privacy-Aware Collaborative and Distributed Bayesian Optimization
Jul 13, 2026Aditya Rane, Sathwik Yamana, Paritosh Ramanan +2Bayesian Optimization Privacy-Utility Tradeoff
School of Industrial Engineering and Management Oklahoma State University, Stillwater, OK, USA
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Jul 8, 2026Vinícius Gabriel Angelozzi, Héber H. ArcoleziAlgorithmic Fairness Group Fairness
Centre Inria de l’Université Grenoble Alpes · ÉTS Montréal, Inria Grenoble
REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality
Jul 7, 2026Taerin Ki, Sunghwan Park, Junyoung Park +1Biometric Identification Electrocardiography
Chung-Ang University Seoul, South Korea
PromptPET: Privacy-Utility Optimized Prompt Obfuscation
Jul 3, 2026Ke Yang, Olivia Figueira, Umar Iqbal +1Privacy-Preserving Language Models Privacy-Utility Tradeoff
University of California, Irvine Irvine, CA, USA · Washington University in St. Louis St. Louis, MO, USA
SovereignNegotiation-Bench: Evaluating User-Owned Personal Agents In Delegated Bargaining Under Privacy, Consent, Evidence, And Institutional Pressure
Jul 2, 2026Dylan Zongmin LiuAutomated Negotiation AI Agent Benchmarks
Stanford University
Predictability as a Fine-Grained Measure for Privacy
Jun 18, 2026Linda Lu, Karthik SridharanDifferential Privacy Privacy-Utility Tradeoff
Cornell University
TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction
Jun 17, 2026Moon Ye-Bin, Nam Hyeon-Woo, Baek Seong-Eun +2Privacy-Preserving Language Models Privacy Leakage in Language Models
Dept. of Electrical Engineering, POSTECH · Grad. School of Artificial Intelligence, POSTECH · Grad. School of Metaverse, KAIST +1
PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
Jun 16, 2026Arshia Ilaty, Hossein Shirazi, Manasi Chitale +5Synthetic Tabular Data Generation Tabular Data Synthesis
San Diego State University, San Diego, CA 92182 USA. · University of California, Irvine, CA 92697 USA.
CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agents
Jun 10, 2026Siyu Shen, Fenghao Xu, Wenrui Diao +1Mobile GUI Automation Privacy-Utility Tradeoff
The Chinese University of Hong Kong · Southeast University · Shandong University
InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization
Jun 4, 2026Xueyang Wu, Siyuan Liu, Kezhuo Yang +1Audio Representation Learning Depression Detection
Shenzhen NeurStar Inc., China · University of York, United Kingdom · Shanghai Jiao Tong University, China
Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Jun 2, 2026Prashant Shekhar, Caroline HowardOnline Advertising Partial Identification
Department of Mathematics Embry-Riddle Aeronautical University Daytona Beach, FL, USA
The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles
Jun 2, 2026Dejan Radovanovic, Maximilian Schirl, Andreas Unterweger +1Time Series Classification Privacy-Utility Tradeoff
Center for Secure Energy Informatics, Salzburg University of Applied Sciences, Puch bei Hallein, Austria · Paris Lodron University of Salzburg, Salzburg, Austria
LLM Anonymization Against Agentic Re-Identification
May 29, 2026Ziwen Li, Jianing Wen, Tianshi LiPrivacy-Preserving Language Models Privacy-Utility Tradeoff
Khoury College of Computer Sciences Northeastern University Boston, MA
Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence
May 22, 2026Yannik Dittmar, Marvin Jerome Stephan, Thomas Völkl +2Privacy Auditing Privacy-Preserving Language Models
Hasso Plattner Institute, University of Potsdam Potsdam, Germany · TU Darmstadt, Secure Mobile Networking Lab Darmstadt, Germany · TU Darmstadt, Secure Mobile Networking Lab, Darmstadt, Germany +1
It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
May 18, 2026Sangwoo Park, Woongyeong Yeo, Seanie Lee +6Contextual Integrity LLM Alignment
KAIST
PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems
May 15, 2026Shafizur Rahman Seeam, Zhengxiong Li, Zhiyuan Yu +3Data Leakage Privacy-Preserving Language Models
Ian · Rochester Institute of Technology, Rochester, NY, USA · University of Colorado Denver, Denver, CO, USA +2
The Privacy Price of Tail-Risk Learning: Effective Tail Sample Size in Differentially Private CVaR Optimization
May 15, 2026El Mustapha MansouriStochastic Optimization Differential Privacy
School of Engineering, Institute of Science Tokyo, Yokohama, Kanagawa 226-8501, Japan
Limits of Personalizing Differential Privacy Budgets
May 13, 2026Edwige Cyffers, Juba ZianiDifferential Privacy Privacy-Utility Tradeoff
CNRS, LAMSADE, Dauphine-PSL · Georgia Institute of Technology.
Revisiting Privacy Preservation in Brain-Computer Interfaces: Conceptual Boundaries, Risk Pathways, and a Protection-Strength Grading Framework
May 12, 2026Lei Sun, Xiuqing Mao, Shuai Zhang +4Brain-Computer Interfaces Privacy-Preserving ML
PLA Information Engineering University, Zhengzhou, 450001, Henan, China
Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
May 11, 2026Tianyuan Zou, Liang Yue, Yang Liu +2Privacy-Preserving ML Video Surveillance
Institute for AI Industry Research, Tsinghua University, Beijing, China · RayNeo.AI, Shenzhen, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
May 8, 2026Xiao Tian, Jue Fan, Rachael Hwee Ling Sim +1Differentially Private Stochastic Gradient Descent Differential Privacy
Department of Computer Science, National University of Singapore · Agency for Science, Technology and Research (A*STAR), Singapore

Sep 30, 2026, cs.CRJ/K move · Enter open · S save

PrivCert: Certifying Statement Support under Differential Privacy

Tsubasa Takahashi, Takumi Hiraoka

Acompany Co., Ltd.

Differential Privacy Privacy-Utility Tradeoff

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Differentially private (DP) text generation can protect individual records, but privacy alone does not specify what evidence a released statement carries about the underlying data. We identify this as an evidence gap: a private report may contain plausible claims without indicating whether they are strongly supported by the private dataset. We introduce PrivCert, a framework for privacy-preserving reporting that makes statement support explicit through privacy-preserving certificates and emit-or-abstain decisions. As a canonical instantiation, PrivCert-PF (Proposal-and-Filter) separates data-independent candidate discovery from private support certification, emitting only statements whose support passes a private evidence test. We provide theoretical grounding for this framework by characterizing the limits of implicit evidence under DP, deriving a sharp privacy--honesty frontier for single-statement certification, and establishing a worst-case cost for fine-grained multi-statement certification. Experiments on synthetic tasks and TAB, WildChat, and Yelp show that explicit certification maintains low unsupported emission, while free-text DP baselines frequently produce low-support claims under the same declared support semantics. We further show that the PrivCert contract can be realized with histogram, sparse-vector, and Gaussian mechanisms, and use DP synthetic data to illustrate an important boundary: support in a private proxy does not automatically certify support in the original data. Together, these results position privacy-preserving reporting as an evidence-design problem: not only how to generate private text, but what a private report can substantiate about its underlying data.

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