Update multi-party-data.md

Author: grbury

articles/confidential-computing/multi-party-data.md

@@ -24,11 +24,11 @@ ms.author: grbury
 
 # Cleanroom and Multi-party Data Analytics
 
-Azure confidential computing (ACC) provides a foundation for solutions that enable multiple parties to collaborate on data. There are various approaches to solutions, and a growing ecosystem of partners to help enable Azure customers, researchers, data scientists and data providers to collaborate on data while preserving privacy. This article overviews some of the approaches and existing solutions that can be used, all running on ACC.
+Azure confidential computing (ACC) provides a foundation for solutions that enable multiple parties to collaborate on data. There are various approaches to solutions, and a growing ecosystem of partners to help enable Azure customers, researchers, data scientists and data providers to collaborate on data while preserving privacy. This overview covers some of the approaches and existing solutions that can be used, all running on ACC.
 
 ## What are the data and model protections?
 
-Data cleanroom solutions typically offer a means for one or more data providers to combine data for processing.  There is typically agreed upon code, queries, or models that are created by one of the providers or another participant, such as a researcher or solution provider. In many cases, the data can be considered sensitive and undesired to directly share to other participants – whether another data provider, a researcher, or solution vendor. To help ensure security and privacy on both the data and models used within data cleanrooms, confidential computing can be used to cryptographically verify that participants don't have access to the data or models, including during processing. By using ACC, the solutions can bring protections on the data and model IP from the cloud operator, solution provider, and data collaboration participants.
+Data cleanroom solutions typically offer a means for one or more data providers to combine data for processing.  There's typically agreed upon code, queries, or models that are created by one of the providers or another participant, such as a researcher or solution provider. In many cases, the data can be considered sensitive and undesired to directly share to other participants – whether another data provider, a researcher, or solution vendor. To help ensure security and privacy on both the data and models used within data cleanrooms, confidential computing can be used to cryptographically verify that participants don't have access to the data or models, including during processing. By using ACC, the solutions can bring protections on the data and model IP from the cloud operator, solution provider, and data collaboration participants.
 
 ## What are examples of industry use cases?
 
@@ -60,9 +60,9 @@ _Zero-trust participation:_ A major differentiator in confidential cleanrooms is
 
 _Federated learning:_ Federated learning involves creating or using a solution whereas models process in the data owner's tenant, and insights are aggregated in a central tenant. In some cases, the models can even be run on data outside of Azure, with model aggregation still occurring in Azure. Many times, federated learning iterates on data many times as the parameters of the model improve after insights are aggregated. The iteration costs and quality of the model should be factored into the solution and expected outcomes.
 
-_Data residency and sources:_ Customers have data stored in multiple clouds and on-premises.  Collaboration can include data and models from different sources.  Cleanroom solutions can facilitate data and models coming to Azure from these other locations. When data cannot move to Azure from an on-premises data store, some cleanroom solutions can run on site where the data resides.  Management and policies can be powered by a common solution provider, where available.
+_Data residency and sources:_ Customers have data stored in multiple clouds and on-premises.  Collaboration can include data and models from different sources.  Cleanroom solutions can facilitate data and models coming to Azure from these other locations. When data can't move to Azure from an on-premises data store, some cleanroom solutions can run on site where the data resides.  Management and policies can be powered by a common solution provider, where available.
 
-_Code integrity and confidential ledgers:_ With distributed ledger technology (DLT) running on Azure confidential computing, solutions can be built that run on a network across organizations. The code logic and analytic rules can be added only when there's concensus across the various participants. All updates to the code are recorded for auditing via tamper-proof logging enabled with Azure confidential computing.
+_Code integrity and confidential ledgers:_ With distributed ledger technology (DLT) running on Azure confidential computing, solutions can be built that run on a network across organizations. The code logic and analytic rules can be added only when there's consensus across the various participants. All updates to the code are recorded for auditing via tamper-proof logging enabled with Azure confidential computing.
 
 ## What are options to get started?