Scalable contribution bounding to achieve privacy
Skip to main content
Explore our many areas of focus
Explore all research areas
Applied AI & sciences
Earth AI
Health AI
Science AI
Sustainability & crisis resilience
Foundational ML & algorithms
Algorithms & theory
Information retrieval
Machine intelligence
Machine perception
Natural language processing
People, systems & quantum AI
Human-computer interaction and visualization
Networking
Quantum AI
Responsible AI
Anti abuse
Software engineering
Software systems
Learn More
Publications
Projects
Building a collaborative ecosystem
Datasets
Access high-quality datasets to accelerate your research.
Tools & services
Explore our latest AI models and products.
Open source
Discover open-source code and collaborate with the community.
Shaping the future together
See all programs
Faculty programs
Participating in the academic research community through meaningful engagement with university faculty.
Student programs
Supporting the next generation of researchers through a wide range of programming.
Locations
Find your place in our global offices and research labs.
Translating discovery into real-world impact
People
Our researchers drive advancements in computer science through both fundamental and applied research.
Teams
Collaborative groups tackling the world's most challenging AI problems.
Research
Explore our many areas of focus
Explore all research areas
Applied AI & sciences
Earth AI
Health AI
Science AI
Sustainability & crisis resilience
Foundational ML & algorithms
Algorithms & theory
Information retrieval
Machine intelligence
Machine perception
Natural language processing
People, systems & quantum AI
Human-computer interaction and visualization
Networking
Quantum AI
Responsible AI
Anti abuse
Software engineering
Software systems
Learn More
Publications
Projects
Resources
Building a collaborative ecosystem
Datasets
Access high-quality datasets to accelerate your research.
Tools & services
Explore our latest AI models and products.
Open source
Discover open-source code and collaborate with the community.
Conferences & events
Careers
Shaping the future together
See all programs
Faculty programs
Participating in the academic research community through meaningful engagement with university faculty.
Student programs
Supporting the next generation of researchers through a wide range of programming.
Locations
Find your place in our global offices and research labs.
Blog
About
Translating discovery into real-world impact
People
Our researchers drive advancements in computer science through both fundamental and applied research.
Teams
Collaborative groups tackling the world's most challenging AI problems.
Google
Research
Google AI
Learn about all our AI
Google DeepMind
Explore the frontier of AI
Google Labs
Try our AI experiments
Research
Resources
Conferences & events
Careers
Blog
About
Search
Home
Publications
Scalable contribution bounding to achieve privacy
Morteza Zadimoghaddam
Jason Lee
Alessandro Epasto
Vincent Cohen-Addad
2025
Download
Google Scholar
Copy Bibtex
Abstract
In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bounding" becomes a significant combinatorial challenge. Existing sequential algorithms for this task are computationally intensive and do not scale to the massive datasets prevalent today. To address this scalability bottleneck, we propose a novel and efficient distributed algorithm. Our approach models the complex ownership structure as a hypergraph, where users are vertices and records are hyperedges. The algorithm proceeds in rounds, allowing users to propose records in parallel. A record is added to the final dataset only if all its owners unanimously agree, thereby ensuring that no user's predefined contribution limit is violated. This method aims to maximize the size of the resulting dataset for high utility while providing a practical, scalable solution for implementing user-level privacy in large, real-world systems.
Research Areas
Algorithms and theory
Anti abuse
Meet the teams driving innovation
Our teams advance the state of the art through research, systems engineering, and collaboration across Google.
See our teams
Follow us
Explore our other initiatives
Google AI
Discover how Google AI is committed to enriching knowledge and solving complex challenges
Products
Build
Research
Responsibility
Societal Impact
About
Google Cloud
High-performance infrastructure for cloud computing, data analytics & machine learning
Overview
Solutions
Products
Pricing
Resources
Google DeepMind
Our mission is to build AI responsibly to benefit humanity
Models
Research
Science
About
Google Labs
Explore the future of AI responsibly with Google Labs
About
Experiments
Stay connected
About Google
Google Products
Privacy
Terms
Cookies management controls
×