Distributed trust architecture is becoming the new foundation for data sharing and analytics. It involves shifting trust from local and institutional models to distributed models using technologies like blockchain. This allows data and machine learning models to be distributed across organizations and jurisdictions with privacy, transparency, and integrity maintained through distributed ledgers and smart contracts. CSIRO's Data61 is doing extensive research in this area, including applying these principles to federated learning, software supply chains, consumer data rights frameworks, and other domains to enable innovative uses of data while maintaining user trust.

1. Australia’s National Science Agency
Distributed Trust Architecture:
The New Foundation of Everything
Dr Liming Zhu
Research Director, CSIRO’s Data61
Professor, University of New South Wales
Chair, Blockchain & Distributed Ledger Technology, Standards Australia
Expert on working groups:
• ISO/IEC JTC 1/WG 13 Trustworthiness
• ISO/IEC JTC 1/SC 42/WG 3 - Artificial intelligence – Trustworthiness

2. CSIRO’s Data61: Australia’s Largest Data & Digital
Innovation R&D Organisation
1000+
talented people
(including
affiliates/students)
Home of
Australia’s
National AI
Centre
Data61
Generated
18+ Spin-outs
130+ Patent
groups
200+
Gov &
Corporate
partners
Facilities
Mixed-Reality Lab
Robotics Inno. Centre
AI4Cyber HPC Enclave
300+
PhD students
30+
University collaborators
Responsible
Tech/AI
Privacy & RegTech
Engineering & Design of
AI Systems
Resilient &
Recovery Tech
Cybersecurity
Digital Twin
Spark (bushfire) toolkit
2 |

3. • Blockchain & DLT Standards
– ISO/TC 307
• Government Advisory
• Industry Projects & Technology
• Reports, Books
Blockchain Work at Data61
• Research
– Models and architecture for
blockchain-based systems
– Business process & blockchain
– Trustworthy blockchain
3 |

4. Trust Shifts to Distributed Trust
Local à Institutional à Distributed
Culture, “distributed trust” in
data, ML model, systems,
individuals and organisations
4 |

5. Trust Architecture
5 |
Systems Operating in the Context of
• Zero Trust Environment
• Trustless Machines/Protocols
• Distributed Trust/Blockchain
• Distributed Infrastructure
• Data, Compute/Code, Models

6. § More sources & types from public or partners
§ Decentralized, Distributed, Federated
§ Access and use of sensitive data from another
organization/country
§ Privacy, but also commercial and other sensitivity
§ ML/Analytics over encrypted data
§ ”sharing without access”
§ Open data/innovation (anonymized or desensitized data)
Bi-directional Distributed Trust in Data Sharing
Data sharing, Data-as-a-service & Model-as-a-Service
6 |

7. Trust Architecture via Regulation/Ethics Overlay
Platforms, Risk-based Approach, Market architecture…
Legislations
• EU’s GDPR: privacy, security and “specific” purpose of use
• Australia
• Data Breach Notification Scheme
• Consumer Data Right (CDR): Open Banking, Energy..
• AI Regulations
Ethics Principles and Guidelines
• Trust Data/AI-powered Service provided/run by others?
• OECD/GPAI, UN, Standards…
• Australia: AI Ethics Frameworks and Guidelines
7 |

8. Distributed Trust Architecture in AI Engineering/Systems
8 |
• Entanglements, Correction Cascades,
Undeclared Customers
• Data (Model, Code, Config..) Dependencies
• Anti-patterns
• Debt: Abstraction, Reproducibility, Process
Management, Culture
Circa 2014-15 2020-2021/Today
• ”federated data collection, storage, model,
and infrastructure”
• “co-design and co-versioning”…
• implication of foundation models

9. Distributed Trust in Software Supply Chain
9 |
Circa 2014-15 2020-2021/Today

10. 42 Shades of Grey in (Distributed) Trust Architecture
10 |

11. 1. Y Liu, Q Lu, HY Paik, L Zhu, Defining Blockchain Governance Principles: A Comprehensive Framework. (2021) https://arxiv.org/abs/2110.13374
2. M Qi, Z Wang, F Wu, R Hanson, S Chen, Y Xiang, L Zhu: Blockchain-Enabled Federated Learning Model for Privacy Preservation: System Design ACISP 2021
3. S. Lo, Y. Liu, Q. Lu, C. Wang, X. Xu, H.Paik, L. Zhu: Blockchain-based Trustworthy Federated Learning Architecture. (2021) https://arxiv.org/abs/2108.06912
4. S.Lo, Q. Lu, L. Zhu, H. Paik, X. Xu, C. Wang: Architectural Patterns for the Design of Federated Learning Systems. (2021) https://arxiv.org/abs/2101.02373
5. W Zhang, Q. Lu, et al.: Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT. IEEE Internet Things J. 8(7): 5926-5937 (2021)
6. Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu: FLRA: A Reference Architecture for Federated Learning Systems. ECSA 2021: 83-98
7. Q Lu, X Xu, HMN Bandara, S Chen, L Zhu: Design Patterns for Blockchain-Based Payment Applications (2021) https://arxiv.org/abs/2102.09810
8. Su Yen Chia, Xiwei Xu, Hye-Young Paik, Liming Zhu: Analysing and extending privacy patterns with architectural context. SAC 2021
9. Y. Shanmugarasa, H. Paik, S. Kanhere, Liming Zhu: Towards Automated Data Sharing in Personal Data Stores. PerCom Workshops 2021: 328-331
10. L. Zhu, X. Xu, Q. Lu, et al.: “AI and Ethics - Operationalising Responsible AI”, Humanity Driven AI (2021) https://arxiv.org/abs/2105.08867
11. M Dong, F Yuan, L Yao, X Wang, X Xu, L Zhu: Trust in recommender systems: A deep learning perspective (2020), https://arxiv.org/abs/2004.03774
12. Y. Gao, M. Kim, S. Abuadbba, et al.: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things. SRDS 2020: 91-100
13. Dongyao Wu, Sherif Sakr, Liming Zhu et al.: HDM-MC in-Action: A Framework for Big Data Analytics across Multiple Clusters. ICDCS 2018
14. Yun Zhang, Liming Zhu, Xiwei Xu, Shiping Chen, An Binh Tran: Data Service API Design for Data Analytics. SCC 2018: 87-102
15. S. Lee, Ross J., L. Zhu, "A Contingency-Based Approach to Data Governance Design for Platform Ecosystem", PACIS 2018
16. S. Lee, R. Jeffery, L. Zhu, "Data Governance Decisions for Platform Ecosystems", HICSS 2019
17. Dongyao Wu, Sherif Sakr, Liming Zhu, Huijun Wu: Towards Big Data Analytics across Multiple Clusters. CCGrid 2017: 218-227
18. L Bass, R Holz, P Rimba, AB Tran, L Zhu: Securing a deployment pipeline, 2015, RELENG 2015
Based on Selected Data61 Work
11 |

12. • Distributed Trust Architecture is all about the trade-offs.
• Blockchain solves sometimes, but mostly complements & inspires
• Limitations of Crytoeconomics and blockchain
• Persistent plutocracy, suppressing participant interests, discounting
externalities, moving towards politics, state regulation, temporal modulation,
hybridity..
Is Blockchain the Silver Bullet? Yes & No.
12 |
https://arxiv.org/abs/2110.13374

13. Trust Architecture: Untrusted Analytics to Trusted Data
From MIT Enigma to Solid PODS to Data Airlock
• Model to Data; Insights back
• Enabled analytics of sensitive data
• Vetting of insights back
• Case Studies:
• Major government agency
• Genomics
13 |
Data61’s “Data Airlock” Architecture
Blockchain complements via trusted provenance and value redistribution

14. Trust Architecture: Trusted Data to Untrusted Analytics
§ Open release of data to the public
§ Provably private/desensitized data sharing/release for analytics collaboration
§ Quantified risks and mitigation
§ Case Studies: Worked with 30+ Gov agencies
Data61’s R4: Re-identification Risks Ready-Reckoner
14 |
Blockchain complements via trusted provenance and value redistribution

15. Trust Architecture at Scale: Consumer-Driven Sharing
Enabling FinTechs including blockchain-based ones
• Consumer Data Right (CDR): Australia’s legislation
impacting consumer data and its services
• Consumers can authorise 3rd parties to access their data
• Currently designated sectors: Banking, Energy…
• Data61’s (Recent) Role
• Setting Architecture/Data API standards
• Security profiles standards
• Trust Architecture Trade-offs
• Trusted gateway vs. peer-to-peer trust
• Trust in Nodes: Processing-only vs. Processing + Use
https://consumerdatastandards.gov.au
15 |
ACCC Consumer Data Right in Energy Consultation paper:
data access models for energy data, 2019
Enabling a myriad of Fintech blockchain innovations

16. Distributed Trust at Edge: PODS & Privacy Setting Recommendation
16 |
Yashothara Shanmugarasa, Hye-Young Paik, Salil S.
Kanhere, Liming Zhu: Towards Automated Data Sharing in
Personal Data Stores. PerCom Workshops 2021: 328-331
Adding trust usability to blockchain-based PODS

17. Trust Architecture Patterns: Privacy-by-Design
Responsible AI Strategy
17 |
•
Data61 work: Su Yen Chia, Xiwei Xu, Hye-Young Paik, Liming Zhu: Analysing and
extending privacy patterns with architectural context. SAC 2021
GDPR &
Australian Privacy
Principles

18. 18 |
Dongyao Wu, Sherif Sakr, Liming Zhu et al.: HDM-MC in-Action: A Framework for Big Data Analytics across Multiple Clusters. ICDCS 2018
Dongyao Wu, Sherif Sakr, Liming Zhu, Huijun Wu: Towards Big Data Analytics across Multiple Clusters. CCGrid 2017: 218-227
- Horizontal: features are similar but vary in terms of data (different phone, patients)
- Vertical: same sample ID, but different in features (you with data in banks and telcos.. )
Architecture (trust) trade-offs: computation, communication, dependability, maintainability…
Trust Architecture across Clusters
Data systems across heterogenous clusters (vertical/horizontal partition)

19. Data Access Request Management
Trust Architecture: Federated Data/Model Sharing
Data
set
Model
set
Datasets Version Control
Data/Model Discovery & Registries
Data Registry AI Model Registry
Knowledge Based
Linked Data
Model-Data-Code-Config Co-versioning
Process Mgmt Risk Assessment
Data custodian
Data users
Privacy
Ethics
Continuous Monitoring of
Ethical Usage
Immutable Data-Model
Dependency Tracking
Secure API
Synthetic Data
Analytics Ethics
Consent Management
Data/Analytics Management in Org/Dept.
Federated Data Catalogue
Provable Privacy Release
Identity Mgt
(Macrokey)
https://data.gov.au
powered by Data61 MAGDA
Blockchain

20. Trustworthiness: Model/Data Integrity & Provenance
Responsible AI Strategy
20 |
Data61 work: X Xu, C. Wang, J. Wang, et. al. “Improving Trustworthiness of AI-
based Dynamic Digital-Physical Parity” , 2021 (submitted)
• Blockchain improves trust in data integrity
and model integrity
• Provenance is the key

21. When there are cultural or legislative restrictions
in place to data sharing, consider alternatives!
Federated Model: “Data Co-Ops”
• No centralised data repositories
• Edge AI and Analytics
Scientific Approaches
• Zero-knowledge proofs, homomorphic
encryption, secure-multi-party computation
Trust Architecture: Federated ML/Data Analytics
From limited access to full encryption during use
21 |
Other Case Studies at Data61
• Bank + Telco for fraud analytics
• Two gov departments for joint insights
Other Supported Scenarios
• Innovation in secure transactions
• Access to data by regulators
• Cross-border data flow
Data61 work: SK Lo, Q Lu, L Zhu, HY Paik, X Xu, C Wang: Architectural patterns for the
design of federated learning systems, Journal of Systems and Software (2021)
Data61 work: SK Lo, Q Lu, HY Paik, L Zhu, FLRA: A Reference Architecture for Federated
Learning Systems, European Conference on Software Architecture (2021)

22. Use Cases
- keyboard prediction
- browser history recommendation
- visual object detection
- diagnosis and treatment prediction
- drug discovery (across facilities involving IP)
- meta-analysis over distributed medical databases
- augmented reality
Data61 case studies
• name entity resolution
• fraud/anomaly detection (bank + telco)
• crop yield prediction - federated transfer learning
• IIoT fault detection
Federated Learning Architecture & Use Cases

23. Federated Learning: Trust Architecture and Patterns

24. Blockchain-based Trustworthy Federated Learning Architecture
S. Lo, Y. Liu, Q. Lu, C. Wang, X. Xu, H.Paik, L. Zhu: Blockchain-based Trustworthy Federated Learning Architecture. (2021)

25. Blockchain-based Federated Learning for Industrial IoT
W Zhang, Q. Lu, et al.: Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT. IEEE Internet
Things J. 8(7): 5926-5937 (2021)

26. Designing Trust &
Trustworthiness
with Blockchain

27. Where to deploy?
(IV.D)
What incentive?
Need anonymity
mechanism?
Other
design
decisions
(IV.D)
Trust
Decentralization
Has trusted
authority?
Can it be
decentralised?
How to decentralise
the authority?
(IV.A)
Need a new
blockchain ?
What type?
Need multiple
blockchains?
What block size and
frequency?
What consensus
protocol ?
Blockchain
configurations
(IV.C)
What data structure?
Storage and computation:
on-chain vs. off-chain
(IV.B)
Use traditional
database
Yes No
Yes
No
Designing Trust with Blockchain (1/2)
• Design Process, including Suitability Analysis
• A taxonomy of blockchain-based systems for architecture design, X. Xu, I. Weber, M. Staples et al., ICSA2017.
• The blockchain as a software connector, X. Xu, C. Pautasso, L. Zhu et al., WICSA2016.
• Quality Analysis
• Quantifying the cost of distrust: Comparing blockchain and cloud services for business process execution. P.
Rimba, A. B. Tran, I. Weber et al., Information Systems Frontiers, accepted August 2018 (previously SCAC 2017)
• Comparing blockchain and cloud services for business process execution, P. Rimba, A. B. Tran, I. Weber et al.,
ICSA2017.
• Predicting latency of blockchain-based systems using architectural modelling and simulation, R.
Yasaweerasinghelage, M. Staples and I. Weber, ICSA2017.
• Design Patterns https://research.csiro.au/blockchainpatterns/
– A pattern collection for blockchain-based applications. X. Xu, C. Pautasso, L., Q. Lu, and I. Weber, EuroPLoP
2018
• Integration with other systems
– EthDrive: A Peer-to-Peer Data Storage with Provenance, X. L. Yu, X. Xu, B. Liu, CAISE2017.
Design process, quality analysis, design patterns and governance/risks
ICSOC18: Distributed Trust | Liming Zhu 27

28. Designing Trust with Blockchain (2/2)
• Business Process Execution
• Untrusted business process monitoring and execution using blockchain,
I. Weber, X. Xu, R. Riveret et al., BPM 2016
• Optimized Execution of Business Processes on Blockchain,
L. García-Bañuelos, A. Ponomarev, M. Dumas, Ingo Weber, BPM 2017
• Caterpillar: A blockchain-based business process management system, O. López-Pintado, L. García-
Bañuelos, M. Dumas, and I. Weber, BPM 2017 Demo
• Runtime verification for business processes utilizing the Bitcoin blockchain, C. Prybila, S. Schulte, C.
Hochreiner, and I. Weber, Future Generation Computer Systems (FGCS), accepted August 2017
• Data / Asset Modelling
• Regerator: a Registry Generator for Blockchain, A. B. Tran, X. Xu, I. Weber, CAISE 2017 Demo
• Combined Asset & Process Modelling
• Lorikeet: A Model-Driven Engineering Tool for Blockchain-Based Business Process Execution and
Asset Management A. B. Tran, Q. Lu, I. Weber, BPM 2018 Demo
Cross-org focused, Process/Data/Assets/Artifact-based model-driven engineering
ICSOC18: Distributed Trust | Liming Zhu 28

29. • Digitisation of assets (as tokens) and
distributed trading
– Unlocking finance around illiquid assets
• Interactions between primary
registries (not necessarily blockchain)
and other exchanges/markets
• Many open research issues in law,
regulation, technology, finance
Application: Tokenisation in
Digital Finance
29 |

30. 1) Dynamic Registers for Instant Exchange
creation, issuing, trading, and settlement
of digital assets in real time
2) Advanced Securitisation digitisation of assets
and use of CBDCs in transactions
3) Distributed Trading real-time exchange
4) RegTech with Algorithmic Real-Time Enforcement
governance and compliance in the ‘instant’ asset
transfer environment. New approaches to
regulation, supervision, and operational certainty
Research Programs
30 |

31. Australia’s National Science Agency
Trust Architecture in
Distributed Ecosystem:
Lessons from Responsible
AI/Platforms

32. • Responsible AI: “the development of intelligent systems according to fundamental human
principles and values.” [1]
• Being legal is a minimum requirement for responsibility; the duty you have to others.
• What are these ”Principles”? E.g. AI Ethics Principles. Make sure that “you build the right things”
• How can you be sure in a verifiable way? - “Trustworthy AI” – Make sure “you build in the right ways”
Responsible AI & AI Ethics Principles
Australia’s AI Ethics Principles
1) Human, societal and environmental wellbeing
2) Human-centred values
3) Fairness
4) Privacy protection and security
5) Reliability and safety
6) Transparency and explainability
7) Contestability
8) Accountability
32 |
[1]

33. “It never does just what I want, but only what I tell it.”
• Value alignment problem
• given an optimisation algorithm, how to make sure the
optimisation of its objective function results in outcomes that
we actually want, in all respects? [1]
• impossible (not simply hard) to accurately and completely
specify all the goals, undesirable side-effects and constraints
• sometimes latent requirements
• Autonomy & Agency
• solve problems autonomously , without explicit guidance from a
human being
Responsible AI – What’s unique?
[2] Data61 work: L. Zhu, X. Xu, Q. Lu, G. Governatori, and J. Whittle,
“AI and Ethics - Operationalising Responsible AI”, Humanity Driven AI
(2021). https://arxiv.org/abs/2105.08867
33 |
[1]

34. Create Store Process Archive Delete
Data Manager
create save update move delete
Traditional Life Cycle of Data (ISO/IEC 38505-1)
Collect Manage
Survey/Research/
Productize
Consume Terminate
Data Provider
Data Provider Data Manager Data Analyst Data Consumer
upload manage analysis generate use/share terminate
Open data
Closed data
Shared data
Access
Machine-generated
Human-sourced
Process-mediated
Data source
Raw data
Derived data
Derived insights
Process
For evolving (aggregate, combine data…)
For sharing
Retained data
Withdrawn data
Existence
If derived data,
If shared data,
If retained data,
For reusing (discover)
Proprietary data
Public data
Ownership/Rights
PII data
Non-PII data
Privacy/Sensitivity
Data in a Platform Ecosystem
Responsible Data Usage for Distributed Ecosystems
ICSOC18: Distributed Trust | Liming Zhu 34
S. Lee, Ross J., L. Zhu, "A
Contingency-Based Approach
to Data Governance Design
for Platform Ecosystem",
PACIS 2018
S. Lee, R. Jeffery, L. Zhu, "Data
Governance Decisions for
Platform Ecosystems", HICSS
2019

35. Responsible AI for Distributed Ecosystems
Shneiderman, B.: Bridging the gap between ethics and practice:
Guidelines for reliable, safe, and trustworthy human-centered ai
systems. ACM Trans. Interact. Intell. Syst. 10(4) (2020).
Data61 work: S. Lee, L. Zhu, R. Jeffery “Data Governance Decisions for
Platform Ecosystems” HICSS 2019: 1-10
Industry + Organisation + Teams
Data + Model
35 |

36. Operationalising via Architecture and Process Patterns
Data61 work: L. Zhu, X. Xu, Q. Lu, G. Governatori, and J. Whittle, “AI and
Ethics - Operationalising Responsible AI”, Humanity Driven AI (2021).
https://arxiv.org/abs/2105.08867
Data61 work: Q. Lu, L. Zhu, et.al. “Software engineering for
responsible AI: an empirical study and operationalised
mechanisms” (under review)
36 |

37. Applying the Lessons to Blockchain Governance
37 |
https://arxiv.org/abs/2110.13374

38. Australia’s National Science Agency
Can’t give a talk without
mentioning Metaverse

39. (Distributed) Trust in Metaverse/Digital Twin
The Metaverse is a massively scaled and interoperable network of real-time rendered 3D
virtual worlds which can be experienced synchronously and persistently by an effectively
unlimited number of users with an individual sense of presence, and with continuity of
data, such as identity, history, entitlements, objects, communications, and payments.
-Matthew Ball

40. From Commercial Metaverse to Decentralized Pluriverse
ICSOC18: Distributed Trust | Liming Zhu 40

41. • A digital twin is a digital representation of a physical object. It includes
the model of the physical object, data from the object, a unique one-
to-one correspondence to the object and the ability to monitor the
object.1
• A digital twin is a virtual replica of a physical asset or a process, which
is used for product design, monitoring, simulation, optimization, and
maintenance. It comprises sensors and devices that collect real-time
data from a physical asset.2
• A digital twin is a digital model or replica of a physical asset, product,
process or system that allows a digital footprint of key assets or
products from design and development through the end of the product
lifecycle.3
• ‘A digital twin is a virtual representation of real-world entities and
processes, synchronized at a specified frequency and fidelity.’4
More boring name – Digital-Twin-stan + NPC?
1 Gartner, https://www.gartner.com/smarterwithgartner/how-to-use-digital-twins-in-your-iot-strategy/, Access date 10th January 2021
2 Technavio, 2020, Global Digital Twin Market 2020-2024
3 Frost and Sullivan - TechVision Group of, May 2019, Digital Twin: Application Landscape and Opportunity Assessment
4 Digital Twin Consortium, May 2019, https://blog.digitaltwinconsortium.org/2020/12/digital-twin-consortium-defines-digital-twin.html
Accessed 6th January 2021

42. ‘Urban Scale Digital Twins’ Themes
Example figure depicting Themed Digital Twins, such as water of energy systems
7 George Percivall, OGC CTO, 12th January 2021, Overview of the Location Powers Urban Digital Twin Summit (Keynote
Presentation)

43. ‘National’ Digital Twin – an ecosystem
• ‘The National Digital Twin
will not be a single large
model but an ecosystem of
connected digital twins
which can enable system
optimisation and planning
across sectors and
organisations.’5
Example figure depicting Digital Twin at the precinct scale, and multiple
twins, an ecosystem
5 Centre for Digital Built Britain, May 2020, The approach to delivering a
National Digital twin for the United Kingdom, Summary Report

44. Platform implementations informing perspectives include:
• National Map
• NSW Spatial Digital Twin
• QLD Spatial Digital Twin
Ecosystem Enablers for Urban Scale Digital Twins:
• Collaboration, Governance
• Trusted Data Sharing
• Standards, Formats
• Visualisation, Accessibility
Data61’s Work – Platforms

45. 45 |
DESIGN
“Rules as Code”
to check designs
against
regulations
AI and simulation
for early lifecycle
BUILD
Robotic SLAM
for inspections
“Rules as Code”
to check
compliance
OPERATE
National
Digital Twin
Infrastructure
CONSTRUCTION
PROJECT
FACILITIES
MANAGEMENT
SUPPLY
CHAIN
• Blockchain for
certification
and provenance
• AI for risk-driven inspections
• Smart contracts for project
automation & security of
payments
• Smart sensors
• AI predictive
maintenance
One Multiverse Future – Enabled by Blockchain
BLOCKCHAIN

46. Summary
• Trust architecture is becoming distributed and underpins everything.
• Blockchain solves, complements and inspires.
• Solutions at different levels
• Architecture styles: model-to-data, federated learning … enabled by blockchain
• Design patterns/tactics e.g. https://research.csiro.au/blockchainpatterns/
• “Meta”-level
– Responsible Data and AI for distributed ecosystems
– Metaverse and digital twins
46
Thank you!