Confidential Computing - Revision history

Tom: /* Other Material */

2025-11-03T01:12:11Z

‎Other Material

2025-11-03T01:08:09Z

‎Postgres

2025-11-03T01:07:22Z

‎Postres

2025-11-03T01:06:27Z

‎Google

2025-11-02T23:40:39Z

‎Google

2025-11-02T23:39:54Z

‎Google’s Protected Computing (Broader Framework)

2025-11-02T23:39:29Z

‎Google’s Protected Computing (Broader Framework)

2025-11-02T23:37:14Z

‎Google’s Protected Computing (Broader Framework)

2025-11-02T23:36:52Z

‎Google’s Protected Computing (Broader Framework)

2025-11-02T23:35:54Z

← Older revision		Revision as of 01:12, 3 November 2025
Line 90:		Line 90:
	* [https://github.com/confidential-computing/governance Confidential Computing Consortium Governance Documents]		* [https://github.com/confidential-computing/governance Confidential Computing Consortium Governance Documents]

		+	[[Category: Hardware]]
	[[Category: Security]]		[[Category: Security]]

@@ Line 8: / Line 8: @@
 * [https://www.linkedin.com/feed/update/urn:li:share:7130618269056581632/ Microsoft announces Azure]  [[Confidential Computing]] by Vikas Bhatia
 * 2024-07-01 Process Isolation described in [https://news.dyne.org/privacy-in-eudi/ Privacy in EUDI] by Denis Roio
-===Postgres===
+===PostgreSQL===
 Transforming PostgreSQL into a Confidential Database with Confidential Computing<ref>Microsoft ''Azure Confidential Computing for Azure Database for PostgreSQL (Preview)'' https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/concepts-confidential-computing</ref>

@@ Line 8: / Line 8: @@
 * [https://www.linkedin.com/feed/update/urn:li:share:7130618269056581632/ Microsoft announces Azure]  [[Confidential Computing]] by Vikas Bhatia
 * 2024-07-01 Process Isolation described in [https://news.dyne.org/privacy-in-eudi/ Privacy in EUDI] by Denis Roio
-===Postres===
+===Postgres===
 Transforming PostgreSQL into a Confidential Database with Confidential Computing<ref>Microsoft ''Azure Confidential Computing for Azure Database for PostgreSQL (Preview)'' https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/concepts-confidential-computing</ref>

@@ Line 39: / Line 39: @@
 *Regulatory alignment**: Supports compliance with HIPAA, GDPR, and other data protection laws.
-===Google===
+===Google Protected Computing===
 there is a difference: Confidential Computing is a specific technology focused on protecting data in use via hardware-based isolation, while Google’s Protected Computing is a broader privacy framework that includes Confidential Computing as one of several techniques.

@@ Line 50: / Line 50: @@
 Examples:
+* Confidential VMs: Encrypt memory and isolate workloads.
-Confidential VMs: Encrypt memory and isolate workloads.
+* Confidential GKE Nodes: Extend memory encryption to Kubernetes clusters.
+* Confidential Space: Enables secure multi-party computation.
 Sources: Google Cloud Confidential Computing

@@ Line 68: / Line 68: @@
 * Homomorphic Encryption: Performing computations on encrypted data.
 * Private Compute Core: On-device AI processing (e.g., Smart Reply, Live Translate) isolated from apps and OS.
 * End-to-End Encryption: For services like Android Backup and Google VPN.
 Sources: [https://blog.google/technology/safety-security/how-we-achieve-privacy-through-innovation/ Google’s Protected Computing overview]

@@ Line 60: / Line 60: @@
 ====Google’s Protected Computing (Broader Framework)====
-Definition: Google’s umbrella approach to privacy that includes minimizing data collection, de-identifying data, and restricting access—even from Google itself.
+[https://blog.google/technology/safety-security/how-we-achieve-privacy-through-innovation/ Definition: Google’s umbrella approach to privacy that includes minimizing data collection, de-identifying data, and restricting access—even from Google itself.]
 Components:
+* Confidential Computing (as above)
-Confidential Computing (as above)
+* Federated Learning: Training models on-device without centralizing data.
+* Differential Privacy: Adding statistical noise to protect individual data points.
-Federated Learning: Training models on-device without centralizing data.
+* Homomorphic Encryption: Performing computations on encrypted data.
+* Private Compute Core: On-device AI processing (e.g., Smart Reply, Live Translate) isolated from apps and OS.
 Differential Privacy: Adding statistical noise to protect individual data points.
 End-to-End Encryption: For services like Android Backup and Google VPN.
-Sources: Google’s Protected Computing overview
+Sources: [https://blog.google/technology/safety-security/how-we-achieve-privacy-through-innovation/ Google’s Protected Computing overview]
 Summary of Differences

@@ Line 87: / Line 87: @@
 |-
 |Use Case	 || Secure processing in cloud or edge	 || Holistic privacy across devices, cloud, and services
-|- ||
+|-
-|Provider	Industry-wide concept (Google, Microsoft, AWS, etc.)	 || Google-specific implementation and philosophy
+|Provider	 || Industry-wide concept (Google, Microsoft, AWS, etc.)	 || Google-specific implementation and philosophy
 |-
 |}

@@ Line 80: / Line 80: @@
 Summary of Differences
 {| border="1",spacing ="2"
-|Feature||Confidential Computing	||Protected Computing
+|Feature || Confidential Computing	||Protected Computing
 |-
-|Scope	Narrow (data-in-use protection)	Broad (end-to-end privacy framework)
+|Scope	 || Narrow (data-in-use protection)	 || Broad (end-to-end privacy framework)
 |-
-|Tech Focus	Hardware-based TEEs	Combines multiple PETs (e.g., TEEs, differential privacy, federated learning)
+|Tech Focus	 || Hardware-based TEEs	 || Combines multiple PETs (e.g., TEEs, differential privacy, federated learning)
 |-
-|Use Case	Secure processing in cloud or edge	Holistic privacy across devices, cloud, and services
+|Use Case	 || Secure processing in cloud or edge	 || Holistic privacy across devices, cloud, and services
-|-
+|- ||
-|Provider	Industry-wide concept (Google, Microsoft, AWS, etc.)	Google-specific implementation and philosophy
+|Provider	Industry-wide concept (Google, Microsoft, AWS, etc.)	 || Google-specific implementation and philosophy
 |-
 |}
 ==References==
 <references />