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🗞️Privacy-preserving data sharing now generally available with BigQuery data clean rooms

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Privacy-preserving data sharing now generally available with BigQuery data clean rooms

2024-04-05 - Nikhil Gaekwad (from Google Cloud Blog)

The rise of data collaboration and use of external data sources highlights the need for robust privacy and compliance measures. In this evolving data ecosystem, businesses are turning to clean rooms to share data in  low-trust environments. Clean rooms enable secure analysis of sensitive data assets, allowing organizations to unlock insights without compromising on privacy. To facilitate this type of data collaboration, we launched the preview of data clean rooms last year. Today, we are excited to announce that BigQuery data clean rooms is now generally available. Backed by BigQuery, customers can now share data in place with analysis rules to protect the underlying data. This launch includes a streamlined data contributor and subscriber experience in the Google Cloud console, as well as highly requested capabilities such as: Join restrictions: Limits the joins that can be on specific columns for data shared in a clean room, preventing unintended or unauthorized connections between data.  Differential privacy analysis rule: Enforces that all queries on your shared data use differential privacy with the parameters that you specify. The privacy budget that you specify also prevents further queries on that data when the budget is exhausted. List overlap analysis rule: Restricts the output to only display the intersecting rows between two or more views joined in a query. Usage metrics on views: Data owners or contributors see aggregated metrics on the views and tables shared in a clean room. Using data clean rooms in BigQuery does not require creating copies of or moving sensitive data. Instead, the data can be shared directly from your BigQuery project and you remain in full control. Any updates you make to your shared data are reflected in the clean room in real-time, ensuring everyone is working with the most current data. Create and deploy clean rooms in BigQuery BigQuery data clean rooms are available in all BigQuery regions. You can set up a clean room environment using the Google Cloud console or using APIs. During this process, you set permissions and invite collaborators within or outside organizational boundaries to contribute or subscribe to the data. Enforce analysis rules to protect underlying data When sharing data into a clean room, you can configure analysis rules to protect the underlying data and determine how the data can be analyzed. BigQuery data clean rooms support multiple analysis rules including aggregation, differential privacy, list overlap, and join restrictions. The new user experience within Cloud console lets data contributors configure these rules without needing to use SQL. Lastly, by default, a clean room employs restricted egress to prevent subscribers from exporting or copying the underlying data. However, data contributors can choose to allow the export and copying of query results for specific use cases, such as activation. Monitor usage and stay in control of your data The data owner or contributor is always in control of their respective data in a clean room. At any time, a data contributor can revoke access to their data. Additionally, as the clean room owner, you can adjust access using subscription management or privacy budgets to prevent subscribers from performing further analysis. Additionally, data contributors receive aggregated logs and metrics, giving them insights into how their data is being used within the clean room. This promotes both transparency and a clearer understanding of the collaborative process.  What BigQuery data clean room customers are saying  Customers across all industries are already seeing tremendous success with BigQuery data clean rooms. Here’s what some of our early adopters and partners had to say: “With BigQuery data clean rooms, we are now able to share and monetize more impactful data with our partners while maintaining our customers' and strategic data protection.” -  Guillaume Blaquiere, Group Data Architect, Carrefour “Data clean rooms in BigQuery is a real accelerator for L'Oréal to be able to share, consume, and manage data in a secure and sustainable way with our partners.” -  Antoine Castex, Enterprise Data Architect, L’Oréal “BigQuery data clean rooms equip marketing teams with a powerful tool for advancing privacy-focused data collaboration and advanced analytics in the face of growing signal loss. LiveRamp and Habu, which independently were each early partners of BigQuery data clean rooms, are excited to build on top of this foundation with our combined interoperable solutions: a powerful application layer, powered by Habu, accelerates the speed to value for technical and business users alike, while cloud-native identity, powered by RampID in Google Cloud, maximizes data fidelity and ecosystem connectivity for all collaborators. With BigQuery data clean rooms, enterprises will be empowered to drive more strategic decisions with actionable, data-driven insights.” - Roopak Gupta, VP of Engineering, LiveRamp “In today’s marketing landscape, where resources are limited and the ecosystem is fragmented, solutions like the data clean room we are building with Google Cloud can help reduce friction for our clients. This collaborative clean room ensures privacy and security while allowing Stagwell to integrate our proprietary data to create custom audiences across our product and service offerings in the Stagwell Marketing Cloud. With the continued partnership of Google Cloud, we can offer our clients integrated Media Studio solutions that connect brands with relevant audiences, improving customer journeys and making media spend more efficient.” - Mansoor Basha, Chief Technology Officer, Stagwell Marketing Cloud “We are extremely excited about the General Availability announcement of BigQuery data clean rooms. It's been great collaborating with Google Cloud on this initiative and it is great to see it come to market.. This release enables production-grade secure data collaboration for the media and advertising industry, unlocking more interoperable planning, activation and measurement use cases for our ecosystem.” - Bosko Milekic, Chief Product Officer, Optable Next steps Whether you're an advertiser trying to optimize your advertising effectiveness with a publisher, or a retailer improving your promotional strategy with a CPG, BigQuery data clean rooms can help. Get started today by using this guide, starting a free trial with BigQuery, or contacting the Google Cloud sales team.

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[Technology] 🌎 https://cloud.google.com/blog/products/data-analytics/bigquery-data-clean-rooms-now-generally-available/ [🧠] [v2] article_embedding_description: {:llm_project_id=>"Unavailable", :llm_dimensions=>nil, :article_size=>12173, :llm_embeddings_model_name=>"textembedding-gecko"}
[🧠] [v1/3] title_embedding_description: {:ricc_notes=>"[embed-v3] Fixed on 9oct24. Only seems incompatible at first glance with embed v1.", :llm_project_id=>"unavailable possibly not using Vertex", :llm_dimensions=>nil, :article_size=>12173, :poly_field=>"title", :llm_embeddings_model_name=>"textembedding-gecko"}
[🧠] [v1/3] summary_embedding_description: {:ricc_notes=>"[embed-v3] Fixed on 9oct24. Only seems incompatible at first glance with embed v1.", :llm_project_id=>"unavailable possibly not using Vertex", :llm_dimensions=>nil, :article_size=>12173, :poly_field=>"summary", :llm_embeddings_model_name=>"textembedding-gecko"}
[🧠] As per bug https://github.com/palladius/gemini-news-crawler/issues/4 we can state this article belongs to titile/summary version: v3 (very few articles updated on 9oct24)

🗿article.to_s 

------------------------------
Title: Privacy-preserving data sharing now generally available with BigQuery data clean rooms
Summary: The rise of data collaboration and use of external data sources highlights the need for robust privacy and compliance measures. In this evolving data ecosystem, businesses are turning to clean rooms to share data in  low-trust environments. Clean rooms enable secure analysis of sensitive data assets, allowing organizations to unlock insights without compromising on privacy.
To facilitate this type of data collaboration, we launched the preview of data clean rooms last year. Today, we are excited to announce that BigQuery data clean rooms is now generally available.
Backed by BigQuery, customers can now share data in place with analysis rules to protect the underlying data. This launch includes a streamlined data contributor and subscriber experience in the Google Cloud console, as well as highly requested capabilities such as:

Join restrictions: Limits the joins that can be on specific columns for data shared in a clean room, preventing unintended or unauthorized connections between data. 
Differential privacy analysis rule: Enforces that all queries on your shared data use differential privacy with the parameters that you specify. The privacy budget that you specify also prevents further queries on that data when the budget is exhausted.
List overlap analysis rule: Restricts the output to only display the intersecting rows between two or more views joined in a query.
Usage metrics on views: Data owners or contributors see aggregated metrics on the views and tables shared in a clean room.








  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Using data clean rooms in BigQuery does not require creating copies of or moving sensitive data. Instead, the data can be shared directly from your BigQuery project and you remain in full control. Any updates you make to your shared data are reflected in the clean room in real-time, ensuring everyone is working with the most current data.
Create and deploy clean rooms in BigQuery
BigQuery data clean rooms are available in all BigQuery regions. You can set up a clean room environment using the Google Cloud console or using APIs. During this process, you set permissions and invite collaborators within or outside organizational boundaries to contribute or subscribe to the data.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Enforce analysis rules to protect underlying data
When sharing data into a clean room, you can configure analysis rules to protect the underlying data and determine how the data can be analyzed. BigQuery data clean rooms support multiple analysis rules including aggregation, differential privacy, list overlap, and join restrictions. The new user experience within Cloud console lets data contributors configure these rules without needing to use SQL. 







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Lastly, by default, a clean room employs restricted egress to prevent subscribers from exporting or copying the underlying data. However, data contributors can choose to allow the export and copying of query results for specific use cases, such as activation.
Monitor usage and stay in control of your data
The data owner or contributor is always in control of their respective data in a clean room. At any time, a data contributor can revoke access to their data. Additionally, as the clean room owner, you can adjust access using subscription management or privacy budgets to prevent subscribers from performing further analysis. Additionally, data contributors receive aggregated logs and metrics, giving them insights into how their data is being used within the clean room. This promotes both transparency and a clearer understanding of the collaborative process. 
What BigQuery data clean room customers are saying 
Customers across all industries are already seeing tremendous success with BigQuery data clean rooms. Here’s what some of our early adopters and partners had to say:
“With BigQuery data clean rooms, we are now able to share and monetize more impactful data with our partners while maintaining our customers' and strategic data protection.” -  Guillaume Blaquiere, Group Data Architect, Carrefour
“Data clean rooms in BigQuery is a real accelerator for L'Oréal to be able to share, consume, and manage data in a secure and sustainable way with our partners.” -  Antoine Castex, Enterprise Data Architect, L’Oréal
“BigQuery data clean rooms equip marketing teams with a powerful tool for advancing privacy-focused data collaboration and advanced analytics in the face of growing signal loss. LiveRamp and Habu, which independently were each early partners of BigQuery data clean rooms, are excited to build on top of this foundation with our combined interoperable solutions: a powerful application layer, powered by Habu, accelerates the speed to value for technical and business users alike, while cloud-native identity, powered by RampID in Google Cloud, maximizes data fidelity and ecosystem connectivity for all collaborators. With BigQuery data clean rooms, enterprises will be empowered to drive more strategic decisions with actionable, data-driven insights.” - Roopak Gupta, VP of Engineering, LiveRamp
“In today’s marketing landscape, where resources are limited and the ecosystem is fragmented, solutions like the data clean room we are building with Google Cloud can help reduce friction for our clients. This collaborative clean room ensures privacy and security while allowing Stagwell to integrate our proprietary data to create custom audiences across our product and service offerings in the Stagwell Marketing Cloud. With the continued partnership of Google Cloud, we can offer our clients integrated Media Studio solutions that connect brands with relevant audiences, improving customer journeys and making media spend more efficient.” - Mansoor Basha, Chief Technology Officer, Stagwell Marketing Cloud
“We are extremely excited about the General Availability announcement of BigQuery data clean rooms. It's been great collaborating with Google Cloud on this initiative and it is great to see it come to market.. This release enables production-grade secure data collaboration for the media and advertising industry, unlocking more interoperable planning, activation and measurement use cases for our ecosystem.” - Bosko Milekic, Chief Product Officer, Optable
Next steps
Whether you're an advertiser trying to optimize your advertising effectiveness with a publisher, or a retailer improving your promotional strategy with a CPG, BigQuery data clean rooms can help. Get started today by using this guide, starting a free trial with BigQuery, or contacting the Google Cloud sales team. 

[content]
empty
[/content]

Author: Nikhil Gaekwad
PublishedDate: 2024-04-05
Category: Technology
NewsPaper: Google Cloud Blog
Tags: Data Analytics
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The rise of data collaboration and use of external data sources highlights the need for robust privacy and compliance measures. In this evolving data ecosystem, businesses are turning to clean rooms to share data in  low-trust environments. Clean rooms enable secure analysis of sensitive data assets, allowing organizations to unlock insights without compromising on privacy.

\n

To facilitate this type of data collaboration, we launched the preview of data clean rooms last year. Today, we are excited to announce that BigQuery data clean rooms is now generally available.

\n

Backed by BigQuery, customers can now share data in place with analysis rules to protect the underlying data. This launch includes a streamlined data contributor and subscriber experience in the Google Cloud console, as well as highly requested capabilities such as:

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    \n
  • Join restrictions: Limits the joins that can be on specific columns for data shared in a clean room, preventing unintended or unauthorized connections between data. 
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  • Differential privacy analysis rule: Enforces that all queries on your shared data use differential privacy with the parameters that you specify. The privacy budget that you specify also prevents further queries on that data when the budget is exhausted.
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  • List overlap analysis rule: Restricts the output to only display the intersecting rows between two or more views joined in a query.
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  • Usage metrics on views: Data owners or contributors see aggregated metrics on the views and tables shared in a clean room.
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Using data clean rooms in BigQuery does not require creating copies of or moving sensitive data. Instead, the data can be shared directly from your BigQuery project and you remain in full control. Any updates you make to your shared data are reflected in the clean room in real-time, ensuring everyone is working with the most current data.

\n

Create and deploy clean rooms in BigQuery

\n

BigQuery data clean rooms are available in all BigQuery regions. You can set up a clean room environment using the Google Cloud console or using APIs. During this process, you set permissions and invite collaborators within or outside organizational boundaries to contribute or subscribe to the data.

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Enforce analysis rules to protect underlying data

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When sharing data into a clean room, you can configure analysis rules to protect the underlying data and determine how the data can be analyzed. BigQuery data clean rooms support multiple analysis rules including aggregation, differential privacy, list overlap, and join restrictions. The new user experience within Cloud console lets data contributors configure these rules without needing to use SQL.

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Lastly, by default, a clean room employs restricted egress to prevent subscribers from exporting or copying the underlying data. However, data contributors can choose to allow the export and copying of query results for specific use cases, such as activation.

\n

Monitor usage and stay in control of your data

\n

The data owner or contributor is always in control of their respective data in a clean room. At any time, a data contributor can revoke access to their data. Additionally, as the clean room owner, you can adjust access using subscription management or privacy budgets to prevent subscribers from performing further analysis. Additionally, data contributors receive aggregated logs and metrics, giving them insights into how their data is being used within the clean room. This promotes both transparency and a clearer understanding of the collaborative process. 

\n

What BigQuery data clean room customers are saying 

\n

Customers across all industries are already seeing tremendous success with BigQuery data clean rooms. Here’s what some of our early adopters and partners had to say:

\n

“With BigQuery data clean rooms, we are now able to share and monetize more impactful data with our partners while maintaining our customers' and strategic data protection.” -  Guillaume Blaquiere, Group Data Architect, Carrefour

\n

“Data clean rooms in BigQuery is a real accelerator for L'Oréal to be able to share, consume, and manage data in a secure and sustainable way with our partners.” -  Antoine Castex, Enterprise Data Architect, L’Oréal

\n

“BigQuery data clean rooms equip marketing teams with a powerful tool for advancing privacy-focused data collaboration and advanced analytics in the face of growing signal loss. LiveRamp and Habu, which independently were each early partners of BigQuery data clean rooms, are excited to build on top of this foundation with our combined interoperable solutions: a powerful application layer, powered by Habu, accelerates the speed to value for technical and business users alike, while cloud-native identity, powered by RampID in Google Cloud, maximizes data fidelity and ecosystem connectivity for all collaborators. With BigQuery data clean rooms, enterprises will be empowered to drive more strategic decisions with actionable, data-driven insights.” - Roopak Gupta, VP of Engineering, LiveRamp

\n

“In today’s marketing landscape, where resources are limited and the ecosystem is fragmented, solutions like the data clean room we are building with Google Cloud can help reduce friction for our clients. This collaborative clean room ensures privacy and security while allowing Stagwell to integrate our proprietary data to create custom audiences across our product and service offerings in the Stagwell Marketing Cloud. With the continued partnership of Google Cloud, we can offer our clients integrated Media Studio solutions that connect brands with relevant audiences, improving customer journeys and making media spend more efficient.” - Mansoor Basha, Chief Technology Officer, Stagwell Marketing Cloud

\n

“We are extremely excited about the General Availability announcement of BigQuery data clean rooms. It's been great collaborating with Google Cloud on this initiative and it is great to see it come to market.. This release enables production-grade secure data collaboration for the media and advertising industry, unlocking more interoperable planning, activation and measurement use cases for our ecosystem.” - Bosko Milekic, Chief Product Officer, Optable

\n

Next steps

\n

Whether you're an advertiser trying to optimize your advertising effectiveness with a publisher, or a retailer improving your promotional strategy with a CPG, BigQuery data clean rooms can help. Get started today by using this guide, starting a free trial with BigQuery, or contacting the Google Cloud sales team.

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