On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,738 papers · 148 categories

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48 results for “personalized privacy”

Differential Privacy in Personalized Pricing with Nonparametric Demand Modelsstat.ML

This paper addresses privacy issues in personalized pricing using nonparametric demand models.

problem Privacy violation in personalized pricing algorithms with unknown nonparametric demand models.

method Develops algorithms to make pricing decisions and learn demand while ensuring central and local differential privacy.

result Proves near-optimal regret bounds for algorithms with CDP and LDP guarantees.

differential privacy personalized pricing nonparametric demand models regret analysis

2021-09-10abs ↗pdf ↗

Privacy-Preserving Dynamic Personalized Pricing with Demand Learningcs.CR

Paper develops privacy-preserving dynamic pricing policy for e-commerce.

problem Protecting customer privacy in dynamic pricing with personalized information.

method Uses differential privacy framework to develop a privacy-preserving policy.

result Achieves both privacy and performance guarantees in dynamic pricing.

differential privacy dynamic pricing personalized information regret bound e-commerce

2020-09-27abs ↗pdf ↗

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacycs.LG

Generative framework unifies and improves personalized learning and estimation methods.

problem Statistical heterogeneity in client data motivates personalized learning models.

method Generative framework unifies and suggests new personalized learning and estimation algorithms.

result AdaPeD algorithm numerically outperforms known algorithms.

federated learning personalized learning estimation privacy generative framework

2022-07-05abs ↗pdf ↗

Personalized Graph Federated Learning with Differential Privacycs.LG

PGFL framework learns personalized models with differential privacy.

problem Privacy-preserving personalized learning for diverse data.

method Exploits model similarities and differential privacy (zero-concentrated).

result Algorithm converges to optimal solutions with linear time complexity.

graph federated learning differential privacy personalized models cluster-specific solutions zero-concentrated differential privacy

2023-06-10abs ↗pdf ↗

Ranking Differential Privacystat.ML

This paper protects rankings from differential privacy breaches.

problem Leakage of personal information in rankings.

method Develops ε-ranking differential privacy and a multistage ranking algorithm.

result Establishes the connection between Mallows model and ε-ranking differential privacy.

differential privacy rank privacy Mallows model synthetic rankings inference attack

2023-01-02abs ↗pdf ↗

Personalized and Private Peer-to-Peer Machine Learningcs.LG

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully…

2017-05-23abs ↗pdf ↗

On Privacy and Personalization in Cross-Silo Federated Learningcs.LG

Study improves privacy in cross-silo federated learning by personalizing data.

problem Privacy concerns in cross-silo federated learning.

method Introduced sample-level differential privacy for silos, analyzed mean-regularized multi-task learning.

result Mean-regularized multi-task learning is a strong baseline for cross-silo federated learning under stronger privacy requirements.

differential privacy federated learning cross-silo personalization privacy regulations

2022-06-16abs ↗pdf ↗

Private Reinforcement Learning with PAC and Regret Guaranteescs.LG

Private RL algorithm with privacy guarantees for personalized medicine decisions.

problem Privacy-preserving reinforcement learning for personalized medicine decisions.

method Developed a private optimism-based RL algorithm using joint differential privacy (JDP).

result Achieved strong PAC and regret bounds with a privacy guarantee.

private reinforcement learning personalized medicine joint differential privacy PAC bounds regret bounds

2020-09-18abs ↗pdf ↗

Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyondcs.LG

This paper improves privacy and fairness in federated learning by protecting sensitive data and ensuring group fairness.

problem Privacy and fairness issues in federated learning.

method Introduces group privacy through

d

-privacy, a localized form of differential privacy.

result The method provides better group fairness than a global model in federated learning.

federated learning privacy fairness differential privacy group privacy

2023-09-01abs ↗pdf ↗

Generalized Linear Bandits with Local Differential Privacystat.ML

New algorithms protect user data while optimizing personalized decisions.

problem Personalized decision-making with private user data.

method Developed LDP algorithms for stochastic generalized linear bandits using SGD and OLS.

result Achieved the same regret bound as non-privacy settings with LDP.

local differential privacy contextual bandits stochastic gradient descent ordinary least squares personalized decision-making

2021-06-07abs ↗pdf ↗

Individual Privacy Accounting via a Renyi Filtercs.CR

A new method for tighter privacy loss accounting in adaptive analyses.

problem Ensuring individual privacy in adaptive analyses while staying within a privacy budget.

method A personalized privacy loss estimate and a Rényi differential privacy filter.

result Personalized privacy loss accounting can be practical and tighter than existing methods.

privacy accounting adaptive analysis Rényi differential privacy personalized privacy privacy budget

2020-08-25abs ↗pdf ↗

Federated Marginal Personalization for ASR Rescoringcs.CL

FMP improves personalized ASR models on private devices.

problem Limited accuracy and privacy in federated fine-tuning of ASR models.

method FMP estimates global and personalized marginal distributions and adjusts NNLM probabilities.

result FMP achieves modest WER reductions on ASR rescoring tasks.

federated learning personalized models ASR rescoring privacy word error rate

2020-12-01abs ↗pdf ↗

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningstat.ML

Paper improves privacy-accuracy balance in federated learning.

problem Privacy-accuracy tradeoffs in federated learning.

method Personalized federated learning with joint differential privacy.

result Coordination of local and centralized learning improves accuracy while maintaining privacy.

federated learning privacy differential privacy accuracy tradeoffs

2022-02-10abs ↗pdf ↗

Private Mean Estimation with Person-Level Differential Privacycs.DS

Private mean estimation with multiple samples requires a certain number of people to maintain privacy.

problem Private mean estimation with person-level differential privacy for multiple samples.

method The approach involves estimating the mean up to a distance α in ℓ_2-norm under ε-differential privacy, using algorithms based on the clip-and-noise framework and new analyses.

result The necessary and sufficient number of people to estimate the mean up to distance α in ℓ_2-norm is given by a specific formula.

differential privacy mean estimation person-level multiple samples private statistics

2024-05-30abs ↗pdf ↗

Patient-Driven Privacy Control through Generalized Distillationcs.CR

The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to pri…

2016-11-26abs ↗pdf ↗

Privacy-Preserving Banditscs.LG

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's p…

2019-09-10abs ↗pdf ↗

Near-Optimal Differentially Private Reinforcement Learningcs.LG

New algorithm for personalized healthcare with privacy guarantees.

problem Online exploration in reinforcement learning with differential privacy constraints.

method ε-JDP algorithm with privately released exploration bonuses and visitation statistics.

result Regret bound of

O(\sqrt{SAH^2T}+S^2AH^3/ε)

matching information-theoretic lower bound.

Reinforcement Learning Differential Privacy Personalized Healthcare Privacy for Free Regret Bound

2022-12-09abs ↗pdf ↗

Personalized Privacy Amplification via Importance Samplingcs.CR

The paper analyzes and improves privacy in machine learning through importance sampling.

problem Ensuring privacy in machine learning while maintaining utility and efficiency.

method Individualized privacy analysis of importance sampling, proposing two approaches for constructing sampling distributions.

result Proposed approaches optimize privacy-efficiency trade-off and outperform uniform sampling.

privacy amplification importance sampling differential privacy k-means problem coresets

2023-07-05abs ↗pdf ↗

Differentially Private Estimation of Hawkes Processcs.LG

Proposes first privacy-preserving method for estimating Hawkes processes.

problem Estimating point process models with sensitive personal data raises privacy concerns.

method Proposes differential privacy for event stream data and two optimization algorithms.

result Efficiently estimates Hawkes process models with privacy and utility guarantees.

differential privacy Hawkes process point process personal data privacy-preserving

2022-09-15abs ↗pdf ↗

DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixingstat.ML

DP-CDA generates synthetic data to enhance privacy in high-dimensional datasets.

problem Privacy concerns in anonymized datasets, especially in high-dimensional data.

method Randomized mixing of privacy-sensitive data in a class-specific manner with carefully tuned randomness.

result DP-CDA provides stronger privacy guarantees compared to existing methods, maintaining utility.

privacy preservation dataset synthesis randomized mixing machine learning data publishing

2024-11-25abs ↗pdf ↗

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learningcs.LG

LP-FT improves personalized model training in FL by balancing generalization and personalization.

problem Federated Learning struggles with balancing global generalization and local personalization due to non-identical data distributions.

method Adapting Linear Probing followed by full Fine-Tuning (LP-FT) to the FL setting.

result LP-FT outperforms standard fine-tuning in balancing personalization and generalization across various datasets and PFT variants.

Federated Learning Personalized Fine-Tuning Feature Distortion Linear Probing Fine-Tuning

2025-11-16abs ↗pdf ↗

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modelingstat.ML

FGPR uses averaging and SGD for federated $\mathcal{GP}$ regression, excelling in personalization and multi-fidelity modeling.

problem Privacy-preserving multi-fidelity data modeling and personalization.

method Federated Gaussian process framework with averaging and SGD for local computations.

result FGPR converges to a critical point of the full log-likelihood function, excels in personalization and multi-fidelity modeling.

Federated Learning Gaussian Process Personalization Multi-fidelity Privacy-preserving

2021-11-28abs ↗pdf ↗

Federated User Representation Learningcs.LG

Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and resource-efficient way to utilize existing neur…

2019-09-27abs ↗pdf ↗

Differentially private and decentralized randomized power methodcs.LG

This paper enhances privacy-preserving randomized power method for large datasets.

problem Privacy issues in applying randomized power method to large datasets containing personal information.

method Proposes enhanced privacy-preserving variants of the randomized power method, including a variant with reduced noise and a decentralized framework.

result Tighter convergence bounds and empirical comparisons with previous work in real recommendation datasets.

differential privacy randomized power method decentralized large-scale spectral analysis recommendation tasks

2024-11-04abs ↗pdf ↗

FedLog: Personalized Federated Classification with Less Communication and More Flexibilitycs.LG

FedLog reduces communication in federated learning by sharing data summaries.

problem Significant communication overhead in federated learning with large model parameters.

method Shares minimal sufficient statistics via Bayesian inference and differential privacy.

result High learning accuracy with low communication overhead.

federated learning representation learning privacy Bayesian inference differential privacy

2024-07-11abs ↗pdf ↗

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRankcs.CR

Differentially private graph learning via bounded sensitivity PPR.

problem Protecting user data in graph learning algorithms.

method Proposes a sensitivity-bounded personalized PageRank (PPR) algorithm.

result Achieves similar accuracy to non-private algorithms with large degrees.

differential privacy personalized PageRank graph learning sensitivity bound

2022-07-14abs ↗pdf ↗

I Prefer not to Say: Protecting User Consent in Models with Optional Personal Datacs.LG

Protects user privacy in models using optional personal data.

problem Ensuring fairness for users who opt-out of data sharing.

method Formalizes protection requirements, introduces Protected User Consent (PUC), devises data augmentation strategy.

result PUC-compliant models can improve performance without disadvantaging opt-out users.

Privacy Machine Learning User Consent Protected User Consent Data Augmentation

2022-10-25abs ↗pdf ↗

A compressive multi-kernel method for privacy-preserving machine learningcs.LG

New method preserves privacy while improving machine learning accuracy.

problem Privacy-preserving machine learning for daily data.

method Compressive Privacy and multi-kernel method.

result Improved utility classification accuracy with privacy preservation.

privacy-preserving machine learning multi-kernel method compression SNR activity recognition

2021-06-20abs ↗pdf ↗

Locally Differentially Private Naive Bayes Classificationcs.LG

In machine learning, classification models need to be trained in order to predict class labels. When the training data contains personal information about individuals, collecting training data becomes difficult due to privacy concerns. Local differential privacy is a definition to measure the individual privacy when th…

2019-05-03abs ↗pdf ↗

Emotionless: Privacy-Preserving Speech Analysis for Voice Assistantscs.CR

Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional sta…

2019-08-09abs ↗pdf ↗

Towards Optimal Differentially Private Regret Bounds in Linear MDPscs.LG

Improved privacy in RL with near-optimal regret bounds.

problem Privacy-preserving reinforcement learning in personalized decision-making systems.

method Differentially private algorithm based on LSVI-UCB++ with privacy-preserving techniques.

result Achieved a near-optimal regret bound of O(d * sqrt(H^3 * K) + H^(15/4) * d^(7/6) * K^(1/2) / ε).

differential privacy reinforcement learning linear MDPs regret minimization personalized decision-making

2025-04-12abs ↗pdf ↗

Privately Publishable Per-instance Privacycs.CR

The paper analyzes and proposes methods for privately sharing individual privacy losses using per-instance differential privacy.

problem The standard differential privacy framework provides a worst-case bound that may not accurately reflect individual privacy losses.

method The paper analyzes per-instance differential privacy and proposes methods to privately and accurately publish per-instance privacy losses.

result The methods privately and accurately publish per-instance differential privacy losses with minimal additional privacy cost.

differential privacy per-instance privacy private publishing empirical risk minimizer

2021-11-03abs ↗pdf ↗

Personalized explanation in machine learning: A conceptualizationcs.LG

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personali…

2019-01-03abs ↗pdf ↗

Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcarecs.LG

Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.

problem Privacy and security concerns in traditional cloud-centric ML, especially in wearable devices.

method Develops a blockchain-enhanced federated edge learning (BFEL) framework based on FedCurv, incorporating fisher information matrix and public key encryption.

result Significant reduction in communication cost and high efficiency for federated training on non-iid and heterogeneous data.

Blockchain Privacy-preserving Federated Learning Edge Learning Personalized Healthcare

2025-05-31abs ↗pdf ↗

Multi-Agent Conformal Prediction with Personalized Statistical Validitycs.LG

Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.

problem Challenges in uncertainty quantification for multi-agent settings.

method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.

result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.

conformal prediction multi-agent federated learning privacy heterogeneity

2026-05-30abs ↗pdf ↗

A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Lossstat.ML

A new algorithm improves federated learning by combining knowledge distillation and weighted combination loss.

problem Non-IID client data in federated learning leads to model drift and poor generalization.

method pFedKD-WCL integrates knowledge distillation with bi-level optimization to address non-IID challenges.

result pFedKD-WCL outperforms state-of-the-art algorithms in accuracy and convergence speed.

federated learning knowledge distillation non-IID privacy-preserving personalized

2025-04-06abs ↗pdf ↗

Survey of Personalization Techniques for Federated Learningcs.LG

This paper surveys techniques to personalize federated learning models.

problem Personalized models outperform shared models for some clients, reducing participation.

method Surveys recent research on personalizing federated learning models.

result Personalization techniques improve model performance for individual clients.

federated learning personalization statistical heterogeneity machine learning

2020-03-19abs ↗pdf ↗

Active Privacy-utility Trade-off Against a Hypothesis Testing Adversarycs.IT

User releases data to service provider while balancing privacy and utility.

problem Balancing user privacy and service utility in data release.

method Formulated as a Markov decision process (MDP) and solved using deep reinforcement learning (RL).

result Achieved a trade-off between revealing useful information and protecting sensitive data.

privacy reinforcement learning data release hypothesis testing user information

2021-02-16abs ↗pdf ↗

Privacy Amplification by Missing Datastat.ML

Missing data enhances privacy in differential privacy.

problem Privacy preservation in datasets with missing values.

method Formalized missing data as a privacy amplification mechanism within differential privacy.

result Incomplete data can yield privacy amplification for differentially private algorithms.

differential privacy missing data privacy preservation data analysis

2026-02-02abs ↗pdf ↗

Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Componentscs.LG

A new method models user-specific parameters as a low-rank plus sparse component for efficient personalization.

problem Efficient personalization of machine learning models for individual users.

method Meta-learning approach that models network weights as a sum of low-rank and sparse components.

result The proposed method, AMHT-LRS, achieves nearly optimal sample complexity for estimating the low-rank and sparse components.

personalization meta-learning low-rank sparse components sample efficiency

2022-10-07abs ↗pdf ↗

A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learningstat.ML

A new DP algorithm for weighted ERM protects sensitive data in predictive models.

problem Protecting sensitive personal information in predictive models trained via ERM.

method Proposes the first differentially private algorithm for weighted ERM with formal privacy guarantees.

result Demonstrates strong DP guarantees while maintaining robust performance in real-world data.

differential privacy empirical risk minimization weighted empirical risk minimization outcome-weighted learning personalized treatment rules

2023-07-24abs ↗pdf ↗

Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharingcs.IT

This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.

problem Balancing privacy and utility in time-series data sharing from IoT devices.

method Formulated as POMDPs, solved using A2C DRL, evaluated with synthetic and real data.

result Proposed policies achieve a good balance between privacy and utility.

IoT Privacy Time-series Data Sharing Markov Decision Processes

2022-02-11abs ↗pdf ↗

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcs.IR

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model upd…

2019-01-29abs ↗pdf ↗

Federating Recommendations Using Differentially Private Prototypescs.IR

Federated approach protects privacy while learning user patterns.

problem Protecting sensitive data in recommendation systems.

method Differentially private prototypes for global and local models.

result Local adaptation improves recommendation accuracy and relevance.

differential privacy federated learning recommendation systems privacy-preserving machine learning private prototypes

2020-03-01abs ↗pdf ↗

Group Personalized Federated Learningstat.ML

Group personalization improves FL performance in heterogeneous client data.

problem Mitigating client drift in federated learning with heterogeneous data.

method Fine-tuning a global FL model over homogeneous groups of clients, then personalizing each group's model.

result The proposed method achieves superior personalization performance compared to other FL approaches.

federated learning personalized learning heterogeneous data client drift Bayesian hierarchical modeling

2022-10-04abs ↗pdf ↗

AI for Investment: A Platform Disruptionq-fin.GN

AI platforms disrupt investment by personalizing deal sourcing and insights.

problem Lack of scalable, personalized, and privacy-compliant deal sourcing and insights solutions.

method Development of in-house AI platforms that interact directly with funds and learn from interactions.

result AI platforms provide smarter, personalized use cases for funds, offering a competitive advantage.

AI investment personalized deal sourcing privacy-compliance large language models fund automation

2023-09-06abs ↗pdf ↗

Federated Learning with Personalization Layerscs.LG

The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence, improving data privacy. This sharply deviates from traditional machine learning and necessitates the design of algorithms robust to variou…

2019-12-02abs ↗pdf ↗

Private Causal Inferencestat.ML

Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…

2015-12-17abs ↗pdf ↗