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Distributed Trust Architecture: The New Foundation of Everything

Liming Zhu, profile picture
Liming Zhu

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.

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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
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 |
• 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 |
Trust Shifts to Distributed Trust Local à Institutional à Distributed Culture, “distributed trust” in data, ML model, systems, individuals and organisations 4 |
Trust Architecture 5 | Systems Operating in the Context of • Zero Trust Environment • Trustless Machines/Protocols • Distributed Trust/Blockchain • Distributed Infrastructure • Data, Compute/Code, Models
§ 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 |
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 |
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
Distributed Trust in Software Supply Chain 9 | Circa 2014-15 2020-2021/Today
42 Shades of Grey in (Distributed) Trust Architecture 10 |
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 |
• 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
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
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
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
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
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 | 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)
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
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
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)
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
Federated Learning: Trust Architecture and Patterns
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)
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)
Designing Trust & Trustworthiness with Blockchain
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
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
• 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 |
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 |
Australia’s National Science Agency Trust Architecture in Distributed Ecosystem: Lessons from Responsible AI/Platforms
• 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]
“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]
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
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 |
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 |
Applying the Lessons to Blockchain Governance 37 | https://arxiv.org/abs/2110.13374
Australia’s National Science Agency Can’t give a talk without mentioning Metaverse
(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
From Commercial Metaverse to Decentralized Pluriverse ICSOC18: Distributed Trust | Liming Zhu 40
• 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
‘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)
‘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
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 | 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
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!

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Distributed Trust Architecture: The New Foundation of Everything

  • 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)
  • 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!