arXiv research

This work optimizes mean estimation under varying user privacy demands.

Proposes a privacy-preserving recommendation system using matrix factorization and differential privacy.

New privacy mechanism for user-level discrete distributions with reduced penalty.

New algorithms protect user-level privacy in learning tasks.

PriRec preserves privacy in POI recommendation by keeping data and models on users' devices.

This work optimizes mean estimation under varying privacy constraints.

Novel mean estimation method under user-level differential privacy reduces noise in continual mean estimates.

Decor protects decentralized learning models from curious users.

This paper analyzes user-level local differential privacy in distributed systems.

Estimates population mean from user-level data with privacy, accounting for heterogeneity.

The paper explores how to measure and optimize ad reach while maintaining user privacy.

Paper studies user-level differential privacy in federated linear contextual bandits.

This paper presents a method to generate synthetic data with differential privacy to protect user privacy while maintaining data trends.

Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-mi…

With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML…

New algorithm optimizes privacy and utility in multi-task learning with skewed data.

A new algorithm estimates mean under varying user data sizes with local differential privacy.

Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe…

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…

Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.

Develops privacy-preserving methods for longitudinal linear regression.

We propose a cloud-based filter trained to block third parties from uploading privacy-sensitive images of others to online social media. The proposed filter uses Distributed One-Class Learning, which decomposes the cloud-based filter into multiple one-class classifiers. Each one-class classifier captures the properties…

FedUA trains UA models privately without raw data.

Secure federated learning framework resists adversarial users.

Privacy policies are legal documents that describe how a website will collect, use, and distribute a user's data. Unfortunately, such documents are often overly complicated and filled with legal jargon; making it difficult for users to fully grasp what exactly is being collected and why. Our solution to this problem is…

Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for labeling. This presents a possible privacy leak when the training set includes sensitive user data. In this paper, we describe an approach for…

It is becoming increasingly clear that users should own and control their data. Utility providers are also becoming more interested in guaranteeing data privacy. As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community. We intr…

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…

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a Rényi differentially private framework for boundi…

FLAME improves privacy in federated learning without trusted parties.

New methods protect privacy while providing accurate prediction sets.

Paper improves privacy-accuracy balance in federated learning.

Federated approach protects privacy while learning user patterns.

Protects user privacy in models using optional personal data.

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated t…

Private ALS method improves matrix completion with tighter rates and better privacy.

New bounds for LDP with heterogeneous privacy levels guaranteeing high probability of accuracy.

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior anal…

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…

A new framework reduces data upload for image classification while protecting user privacy.

Today, large amounts of valuable data are distributed among millions of user-held devices, such as personal computers, phones, or Internet-of-things devices. Many companies collect such data with the goal of using it for training machine learning models allowing them to improve their services. User-held data is, howeve…

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

Private RL algorithm with privacy guarantees for personalized medicine decisions.

FPL allows users to control their data in federated top-N recommendation.

This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields…

The paper proposes a privacy-preserving algorithm for decentralized learning using public-key cryptography.

APGE protects graph node representations from inference attacks.

The paper addresses privacy-preserving BAI in clinical trials and user studies.