AzureBootcamp2023: Data Clean Rooms & Confidential Computing by David Sturzenegger & Primo Amrein

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We are very happy to be able to offer you once again an exciting lineup including many new speakers, both from the local and international community, consisting of MVPs, Microsoft employees and industry leads, who will speak about specific use cases in the industry as well as the latest developments around services in Azure. Be it real life use cases from Pax insurance, Axpo, Georg Fischer, and Die Mobiliar, a kickstart with FinOps on Azure, Azure Quantum Compute or PowerBI or deep dives on Azure networking - these sessions will provide you with insights around Azure and the opportunity to connect with peers and speakers.

The schedule is still subject to change.

Time DevOps
Room 3.54 Infrastructure
Room 3.53 Future Tech
Room 3.14
0800 ⏰ Registration
0900 ⭐ How we Build Data Clean Rooms on Azure Confidential Computing at Decentriq
🙂 DAVID STURZENEGGER
🙂 PRIMO AMREIN
1010 ☕ Coffee Break
1040 ⭐ Kubernetes @ PAX - DevOps at a Swiss Insurance
🙂 SASCHA SPREITZER
🙂 ESRA DOERKSEN ⭐ Building a Lakehouse Platform on Azure with Databricks
🙂 HANSJÖRG WINGEIER
🙂 MATHIAS HERZOG ⭐ Azure FinOps: The Quiz
🙂 ROLAND KRUMMENACHER
🙂 STEFAN DENK
1135 ⭐ Advanced Analytics with Azure DevOps Dojo
🙂 ARINDAM MITRA
🙂 ADRIAN SENN ⭐ The immutable laws of security
🙂 ALAIN SCHNEITER
🙂 MICHAEL RÜEFLI ⭐ God really plays dice - Introduction to quantum computing with Q#
🙂 FILIP WOJCIESZYN
1220 🍕 Lunch Break
1330 ⭐ Pushing Azure (DevOps) @ Georg Fischer
🙂 MARTIN STANEK ⭐ Azure Networking vNext - How to build modern connectivity for IaaS, PaaS and SaaS
🙂 ERIC BERG ⭐ How can Microsoft Azure help with sustainability? Methods to estimate your cloud’s carbon footprint
🙂 WIBKE SUDHOLT
1425 ⭐ Eventdriven systems on Azure done right
🙂 ROBIN KONRAD ⭐ Azure PaaS, but as private as possible…
🙂 STEPHAN GRABER ⭐ Use the power of OpenAI to leverage your business application
🙂 DAVID SCHNEIDER
1510 ☕ Coffee Break
1540 ⭐ Securing web applications using Azure AD
🙂 DAMIEN BOWDEN ⭐ Azure Virtual Network Manager: The future of network management?
🙂 MARCEL ZEHNER ⭐ Push your Azure tenant to the next level with Power BI
🙂 DENIS SELIMOVIC
1635 ⭐ Fully automated & cloud-native data platform
🙂 TIM GIGER ⭐ More than a facade - Azure API Management “from zeron to hero”
🙂 MICHAEL RÜEFLI ⭐ Develop for inclusion using cognitive services: an Azure story
🙂 ANDRÉ MELANCIA
🙂 KAY SAUTER
1720 🍻 Networking Apéro sponsored by isolutions
⭐️ How we Build Data Clean Rooms on Azure Confidential Computing at Decentriq#
Decentriq is a Zurich based startup developing leading-edge data privacy products for sensitive industries. They have been awarded the startup of the year 2022 award by Microsoft. This presentation will open with an overview of Microsoft’s confidential computing strategy and Azure’s Confidential Compute offering. Decentriq will then present their confidential computing-based data collaboration platform, a deeper dive into the technology as well as use-cases of their customers ranging from banks to pharmaceutical companies. In many cases it would be desirable to combine sensitive datasets from multiple sources to compute anonymous statistics. Examples range from healthcare research to customer analytics to anti-money laundering and marketing. However, the fact that data comes from multiple sources means that at least one party has to disclose sensitve data to another. In practice this usually means that such use-cases are blocked either for legal or lack-of-trust reasons. Confidential computing is a CPU-rooted privacy technology that enables the processing of data while keeping the data inaccessible to all parties - including all participants, the SaaS platform and infrastructure providers. Encryption in-use enables data to stay encrypted also in memory and prevent access by the operating system. Remote attestation enables users to remotely verify that a server indeed runs in confidential computing and even what code it is running.
🙂 DAVID STURZENEGGER ⚡️ Head of Product @ Decentriq
🙂 PRIMO AMREIN ⚡️ Cloud Lead @ Microsoft Switzerland

Transcript

1. 2023
   #azurebootcampch
   

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2. www.azurebootcamp.ch
   www.azurezurichusergroup.com
   https://www.meetup.com/de-DE/
   Azure-Cloud-Bern-User-Group/
   

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3. Manu
   Meyer
   Stefan
   Roth
   Stefan
   Johner
   

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4. https://www.azurebootcamp.ch/sessions/
   

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5. Please be
   on time!
   https://www.azurebootcamp.ch/sessions/
   

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6. «…a non-profit community conference…»
   

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7. https://www.irene-bizic.com/
   Fotographer: Irene
   Staff:
   

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8. Upcoming Events
   §16.05.2023 Azure Zurich: Scale with KEDA and Container Apps
   §07.06.2023 Azure Bern: Manage and Govern your Hybrid Servers using Azure Arc
   §29.08.2023 DotNET Day Switzerland 2023
   §18. – 20.09.2023 Experts Live Europe, Prague
   

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10. Over a decade of helping our
    customers on their journey to/in
    the Microsoft cloud
    We are supporting them
    wherever they are.
    

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11. Modernisation
    Change management
    Strategy
    development
    Governance/
    security
    Compliance/
    placement
    Solution
    design
    Migration
    Compliance
    Security
    Optimisation/
    FinOps
    Support/
    reliability
    Buy-in/
    commitment
    Migration
    strategy (7 Rs)
    Architecture
    Costs/
    benefits
    Set-up of
    landing zone
    Governance
    Business
    continuity
    Operation/
    lifecycle
    Innovation
    Concept Implementation & transformation Operation & optimisation
    Strategy
    Risk management
    Strategy
    approved
    Roadmap
    available
    Rollout
    3
    2
    1
    

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12. 12
    We are the trusted advisor of choice for
    many companies
    

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13. 10:40h, Room 3.14:
    Azure FinOps: The Quiz
    16:35h, Room 3.54:
    Fully automated & cloud-native data platform
    Whole day:
    Swisscom / itnetX booth
    13
    Our presence
    today
    

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14. View Slide

15. Azure Swiss DCs Update
    Primo Amrein – Cloud Lead Microsoft Switzerland
    

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16. Azure Workloads Launched Since Last Bootcamp (1/2)
    • Azure Container Apps in Switzerland North
    Deploy containerized apps without managing complex infrastructure with this fully managed serverless platform service.
    Build and deploy modern apps and microservices at scale. Applications built on Azure Container Apps can dynamically scale
    based on the following characteristics: HTTP traffic, event-driven processing, CPU or memory load.
    • Azure IoT Hub in Switzerland North and West
    Cloud-hosted solution to connect, monitor and manage IoT assets
    • Azure Ultra Disks in Switzerland North
    Highest-performing storage, suited for data-intensive workloads such as SAP HANA, top-tier DBs and transaction-heavy loads
    

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17. • Azure Spring Cloud Service in Switzerland North
    Open-source application framework providing infrastructure support for developing Java applications
    • Azure DNS Private Resolver in Switzerland North
    Enables to query Azure DNS private zones from on-prem environment and vice versa without deploying VM based DNS servers.
    Customers no longer need to provision IaaS based solutions on their Virtual Networks to resolve names registered on Azure
    Private DNS Zones and can do conditional forwarding of domains back to on-prem, multi-cloud and public DNS servers.
    • Lasv3 VMs in Switzerland North
    Storage-optimized VMs: using the local disk on the node attached directly to the VM rather than durable data disks, allowing
    greater IOPS and throughput for workloads. First AMD-based VMs in Swiss DCs.
    Azure Workloads Launched Since Last Bootcamp (2/2)
    

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18. View Slide

19. New Customer Stories
    YouTube Video 1 YouTube Video 2 YouTube Video 3
    German
    English
    AGEFI Article
    in French
    Inside IT Article
    in German
    

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20. Earlier Customer Stories
    • German
    • French
    • English
    • German
    • French
    • English
    

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21. • German
    • French
    • English
    

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22. Azure cloud security is best in class
    More than 650,000
    customers and 90 of
    the Fortune 100 trust
    Microsoft SCI solutions
    Microsoft employs
    +8,500 security experts
    and committed $20B
    in security investment
    over the next 5 years
    In 2020, 9 billion
    malware threats were
    blocked on endpoints by
    Microsoft 365 Defender
    Microsoft processes
    over 24 trillion signals
    every 24 hours
    

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23. Cloud customers
    are increasingly
    looking for ways to
    trust as little as
    possible
    Full control
    over the data
    lifecycle
    Privacy
    Untrusted
    collaboration
    Regulations
    and
    compliance
    Customer
    trust
    

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24. Data protection
    EXISTING ENCRYPTION
    Data at rest
    Encrypt inactive data when stored
    in blob storage, database, etc.
    Data in transit
    Encrypt data that is flowing between
    untrusted public or private networks
    

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25. Azure Confidential Computing
    EXISTING
    ENCRYPTION
    Data at rest
    Encrypt inactive data when stored
    in blob storage, database, etc.
    Data in transit
    Encrypt data that is flowing between
    untrusted public or private networks
    CONFIDENTIAL
    COMPUTING
    Data in use
    Protect/encrypt data that is in use, while
    in RAM, and during computation
    Protect against
    privileged admins or insiders exploiting bugs in the Hypervisor/OS accessing data without customer consent
    In Azure, confidential computing means…
    A hardware root-of-trust, customer verifiable remote attestation, and memory encryption
    

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26. Confidential Cloud
    Data is fully in the
    control of the
    customer at rest, in
    transit, or in use.
    The cloud platform
    provider is outside
    the trusted
    compute base.
    Code running in the
    cloud is protected
    and verified by the
    customer.
    Activity history is
    immutable and
    auditable.
    

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27. Confidential Computing Market
    Hardware & software market
    Multi-party computing
    Financial, healthcare, life sciences and more
    Global market
    Confidential computing TAM
    US$ billion
    2021 2024 2026
    16-18
    52-54
    1.9-2.0
    

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28. Data Clean Rooms powered
    by Azure Confidential Computing
    Azure Bootcamp
    May 2023
    

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29. 29
    Imagine all data silos get unlocked overnight.
    Every start-up founder, every data scientist,
    magically gets access to all data ever
    collected. What'd be the impact? Will this
    double world GDP in 5 years? 10? What if I told
    you we can already do this?
    Elad Verbin
    29
    

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30. 30
    Unlocking all data silos raises legitimate privacy concerns
    PRIVACY VS UTILITY
    “Unlocking all data silos”
    High Utility, Low Privacy
    Today’s situation
    Low Utility, High Privacy
    PRIVACY
    UTILITY
    

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31. 31
    PRIVACY VS UTILITY
    Confidential Computing can improve this trade-off
    PRIVACY
    UTILITY
    CC-based Data Collaboration
    Good Utility, High Privacy
    

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32. 32
    32
    32
    Bill Gates (2019) on how to learn more
    from data while maintaining privacy
    “ What if we had a way to
    collect data but not reveal
    individual records? ”
    Cost
    Compliance
    Security Control
    SENSITIVE DATA
    COLLABORATION CHALLENGES
    32
    

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33. AGENDA
    DATA COLLABORATION
    CONFIDENTIAL COMPUTING
    DECENTRIQ & USE CASES
    1
    2
    3
    33
    

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34. AGENDA
    DATA COLLABORATION
    CONFIDENTIAL COMPUTING
    DECENTRIQ & USE CASES
    1
    2
    3
    34
    

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35. 35
    DATA COLLABORATION
    INSURANCE
    BANK
    External party
    trusted with all
    data (or trust
    each other)
    Only contractual & organisational measures protect the data at the trusted external party
    As the external party has access to all data, there is risk for unintentional or intentional misuse
    Person-level customer data
    Person-level customer data
    Aggregated and anonymous
    customer insights
    Aggregated and anonymous
    customer insights
    INTRODUCTORY EXAMPLE – THE TRADITIONAL WAY
    

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36. 36
    DATA COLLABORATION
    New confidential computing technology makes it impossible for the third party to
    access data or modify the allowed processing operations
    Confidential computing enables technical verification of this fact, also for remote users
    This renders the risk of data leakage & misuse minimal
    Data Clean
    Room
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    INSURANCE
    BANK
    Person-level customer data
    Person-level customer data
    Aggregated and anonymous
    customer insights
    Aggregated and anonymous
    customer insights
    INTRODUCTORY EXAMPLE – WITH CONFIDENTIAL COMPUTING
    

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37. 37
    DATA COLLABORATION
    This collaboration model covers many use-cases: in medical research, in marketing etc.
    Personal data
    Personal data
    Aggregated statistics
    Data Owner B
    Data Owner A
    Data Clean
    Room
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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38. 38
    DECENTRIQ DATA CLEAN ROOMS
    Sensitive data
    Sensitive data
    Data owner
    Analyst
    PYTHON
    SQL
    Data owner
    R
    

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39. 39
    REQUIREMENTS FOR CONFIDENTIAL DATA PROCESSING
    Personal data
    Aggregated statistics
    Data Owner A
    Data Clean
    Room
    As Data Owner, I want….
    1. When the data is “in the box”, nobody should be able to access it (confidentiality)
    2. Nobody should be able to modify the computations I approved to be run (integrity, purpose-limitation)
    3. To be able to remotely verify that 1 & 2 hold such that I don’t need to trust the third party (verifiability)
    Personal data
    Data Owner B
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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40. AGENDA
    DATA COLLABORATION
    CONFIDENTIAL COMPUTING
    DECENTRIQ & USE CASES
    1
    2
    3
    40
    

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41. 41
    Confidential Computing is the protection of data in use by performing
    computation in a CPU-based Trusted Execution Environment (TEE)
    CONFIDENTIAL COMPUTING
    

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42. 42
    Trust
    perimeter
    Intel SGX Feature in Intel
    IceLake Server CPUs
    CONFIDENTIAL COMPUTING
    Confidential Computing is the protection of data in use by performing
    computation in a CPU-based Trusted Execution Environment (TEE)
    

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43. 43
    CONFIDENTIAL COMPUTING
    Can’t access /
    modify data
    Trust
    perimeter
    Hypervisor
    Intel SGX
    Operating System
    App Enclave
    Virtual Machine
    TEEs provide new security primitives
    Run-time Isolation
    • Encryption & isolation of process memory
    • Memory confidentiality and integrity
    Remote Attestation
    • Verify genuine TEE platforms and their
    healthiness
    • Authenticate remotely running enclave code
    Software
    Feature in Intel
    IceLake Server CPUs
    Confidential Computing is the protection of data in use by performing
    computation in a CPU-based Trusted Execution Environment (TEE)
    

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44. 44
    CONFIDENTIAL COMPUTING
    Can’t access /
    modify data
    Trust
    perimeter
    Hypervisor
    Intel SGX
    Operating System
    App Enclave
    Virtual Machine
    Security model: Hardware manufacturer is trusted
    There are VM-isolation versions too
    • AMD SEV/SNP, Intel TDX
    Azure Confidential Computing is the only cloud
    offering VMs allowing proper remote attestation
    Software
    Feature in Intel
    IceLake Server CPUs
    Confidential Computing is the protection of data in use by performing
    computation in a CPU-based Trusted Execution Environment (TEE)
    

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45. 45
    REQUIREMENTS FOR CONFIDENTIAL DATA PROCESSING
    As Data Owner, I want….
    1. When the data is “in the box”, nobody should be able to access it (confidentiality)
    2. Nobody should be able to modify the computations I approved to be run (integrity, purpose-limitation)
    3. To be able to remotely verify that 1 & 2 hold such that I don’t need to trust the third party (verifiability)
    Personal data
    Aggregated statistics
    Data Owner A
    Data Clean
    Room
    Personal data
    Data Owner B
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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46. 46
    Now data can be kept encrypted
    in memory also when computed
    impossible before
    confidential computing
    Data in use
    Data is encrypted when stored
    on hard disks
    standard for decades
    Data at rest
    Data is encrypted when
    transferred (HTTPS/TLS)
    Data in transit
    standard for decades
    CONFIDENTIALITY THROUGH ENCRYPTION OF DATA IN USE
    Data and code of the enclave process are encrypted and integrity
    checked when transferred from CPU to memory.
    Data only ever is decrypted in the CPU and the CPU is virtually impossible to attack.
    Computation
    Data Storage
    Data
    Results
    

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47. 47
    REQUIREMENTS FOR CONFIDENTIAL DATA PROCESSING
    47
    As Data Owner, I want….
    1. When the data is “in the box”, nobody should be able to access it (confidentiality)
    2. Nobody should be able to modify the computations I approved to be run (integrity, purpose-limitation)
    3. To be able to remotely verify that 1 & 2 hold such that I don’t need to trust the third party (verifiability)
    Personal data
    Aggregated statistics
    Data Owner A
    Data Clean
    Room
    Personal data
    Data Owner B
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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48. 48
    certificate
    authority
    Enclave measurement
    Acceptabe hardware configuration
    Data Clean Room definition
    DEFINE THE ALLOWED COMPUTATIONS AND PERMISSIONS
    

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49. 49
    DEFINE THE ALLOWED COMPUTATIONS AND PERMISSIONS
    certificate
    authority
    Enclave measurement
    Acceptabe hardware configuration
    Data Clean Room definition
    

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50. 50
    DECENTRIQ UI - TABLE DEFINITIONS & INPUT SCHEMA
    

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51. 51
    DECENTRIQ UI - COMPUTATIONS
    

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52. 52
    DECENTRIQ UI - USER PERMISSIONS
    

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53. 53
    REQUIREMENTS FOR CONFIDENTIAL DATA PROCESSING
    As Data Owner, I want….
    1. When the data is “in the box”, nobody should be able to access it (confidentiality)
    2. Nobody should be able to modify the computations I approved to be run (integrity, purpose-limitation)
    3. To be able to remotely verify that 1 & 2 hold such that I don’t need to trust the third party (verifiability)
    Personal data
    Aggregated statistics
    Data Owner A
    Data Clean
    Room
    Personal data
    Data Owner B
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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54. 54
    Step 1 – SGX program (enclave) launch
    1. Its binary (compiled code) is hashed. This value
    is the measurement.
    2. A random enclave key-pair is generated.
    3. { measurement, enclave public key } is signed
    with a secret, CPU-specific key. Only SGX
    programs can get this signature. Intel can
    verify this signature.
    VERIFIABILITY THROUGH REMOTE ATTESTATION
    SGX program
    launch
    Measurement
    and enclave key
    pair generation
    Signature with
    CPU key
    Initialization
    User
    Intel SGX
    Trusted execution environment
    

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55. 55
    Step 1 – SGX program (enclave) launch
    1. Its binary (compiled code) is hashed. This value
    is the measurement.
    2. A random enclave key-pair is generated.
    3. { measurement, enclave public key } is signed
    with a secret CPU-specific key. Only SGX
    programs can get this signature. Intel can
    verify this signature.
    Step 2 – Verification
    1. Check signature with Intel to see if it is
    genuine and all security patches were applied
    2. Check if the measurement corresponds to the
    expected value (code authenticity)
    3. User shares own public key to establish secure
    communication
    4. User checks data room definition (purpose
    limitation) & uploads data
    VERIFIABILITY THROUGH REMOTE ATTESTATION
    SGX program
    launch
    Measurement
    and enclave key
    pair generation
    Signature with
    CPU key
    Initialization Verification
    User key
    {measurement,
    enclave public
    key, signature}
    User
    Intel SGX
    Trusted execution environment
    

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56. 56
    RECAP – DATA COLLABORATION REQUIREMENTS
    As Data Owner, I want….
    1. When the data is “in the box”, nobody should be able to access it (confidentiality)
    2. Nobody should be able to modify the computations I approved to be run (integrity, purpose-limitation)
    3. To be able to remotely verify that 1 & 2 hold such that I don’t need to trust the third party (verifiability)
    Personal data
    Aggregated statistics
    Data Owner A
    Data Clean
    Room
    Personal data
    Data Owner B
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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57. 57
    RECAP – CONFIDENTIAL COMPUTING
    New confidential computing technology makes it impossible for the third party to
    access data or modify the allowed processing operations
    Confidential computing enables independent technical verification of this fact, also by remote users
    Personal data
    Aggregated statistics
    Data Owner A
    Data Clean
    Room
    Personal data
    Data Owner B
    Aggregated statistics
    Azure Confidential Computing
    Data cannot be accessed, not even
    by admins
    

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58. AGENDA
    DATA COLLABORATION
    CONFIDENTIAL COMPUTING
    DECENTRIQ & USE CASES
    1
    2
    3
    58
    

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59. 59
    59
    Enabling secure sensitive data
    collaborations via Data Clean Rooms
    59
    | HOTTEST PRIVACY MARTECH SOLUTIONS
    | FOUNDING MEMBER
    Deployed on Azure Confidential Computing
    Trusted and neutral SaaS platform
    (Switzerland-as-a-Service)
    Confidential Computing means no
    one can see your data
    ORGANIZATION 1
    ORGANIZATION 2
    ORGANIZATION N
    

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60. 60
    Data Clean
    Room
    ORGANIZATION 2
    Sensitive data
    ORGANIZATION N
    Sensitive data
    ORGANIZATION 1
    Sensitive data
    DECENTRIQ DATA CLEAN ROOMS
    COLLA B OR A T E
    on sensitive & federated data
    P R OT ECT
    data confidentiality end-to-end
    ST R ICT LY CONT R OL
    how data is processed
    B E INNOV A T IV E
    Use-cases never possible before
    

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61. 61
    HOW DOES IT WORK?
    encrypted data
    Data Clean Room
    Configuration
    Compliant results
    DATA CLEAN
    ROOM
    DATA OWNER 1 DATA OWNER 2 DATA OWNER N
    DATA ANALYST
    encrypted data encrypted data
    DATA CONSUMER
    

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62. 62
    Confidential Computing Hardware
    Input
    Security
    Output
    Privacy
    User
    Experience
    Decentriq UI
    Exploratory sandbox
    Access Controls
    K-anonymity
    filter
    Decentriq API
    Synthetic
    data
    generator
    Differential
    Privacy*
    SQL Python R Docker*
    Intel
    SGX
    AMD
    SEV
    Intel
    TDX*
    NVIDIA
    GPU*
    PLATFORM COMPONENTS
    

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63. 63
    63
    CASE
    STUDY
    Collaborate with an
    insurer to identify next
    best product with an ML
    model keeping customer
    data confidential
    Improve product prediction efficiency
    Predicted new attributes
    TRAINED PRODUCT
    ML MODEL
    customer
    data
    customer
    data
    BANK INSURER
    

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64. 64
    64
    CASE
    STUDY
    Benchmarking against
    confidential data without
    a trusted 3rd party
    Extended scope to more sensitive data
    Data validation and governance
    INDIVIDUALIZED
    BENCHMARKS
    Queries &
    Reference
    data
    Confidential
    Sales data
    Healthcare
    Consulting PHARMA 2
    PHARMA 1
    PHARMA 20
    

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65. 65
    65
    CASE
    STUDY
    Combine confidential
    phishing emails to
    improve cyber defence
    Compute benchmarks and run NLP models on phishing
    email text data
    Emails remain confidential
    SIX
    SWISS NATIONAL
    BANK
    P H I S H I N G
    D A T A
    ZKB
    P H I S H I N G
    D A T A
    P H I S H I N G
    D A T A
    

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66. 66
    66
    CASE
    STUDY
    Optimize marketing
    campaigns without data
    sharing
    Target right audience across publishers without data sharing
    Unveil new audience insights
    Increased targeting reach based on lookalikes
    TOP AFFINITY
    SEGMENTS FOR
    ADV CAMPAIGN
    customer
    data
    customer
    data
    CAR RESELLER PUBLISHER 2
    PUBLISHER 1
    PUBLISHER 3
    

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67. 68
    INDIVIDUALIZED CARE
    FOR CVD
    PUBLIC
    PARTNERS
    Data Clean
    Room
    INDUSTRY
    PARTNERS
    OBJECTIVE
    Improve personalized care delivery
    by developing treatment
    recommendation models for
    cardiovascular disease (CVD)
    patients
    APPROACH
    Partners will validate models on the
    largest CVD patient data set (>1M
    patients). The ecosystem will be
    open to the wider clinical
    community in the future.
    DECENTRIQ
    Decentriq will be the main analysis
    platform of the collaboration
    

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68. 69
    Take-Aways
    Confidential Computing is a CPU-based technology
    enabling encryption-in-use and remote attestation
    Confidential Computing improves privacy-utility
    trade-offs in data collaborations
    The Decentriq Platform on Azure Confidential
    Computing makes this technology easy to use
    

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