This document provides an overview of Data61, Australia's national science agency, and its work in emerging technologies related to data sharing and analytics. It discusses Data61's strategic goals and focus areas such as artificial intelligence, cybersecurity, digital agriculture, and quantum technologies. It also summarizes Data61's work establishing Australia's National AI Centre and its research on topics like blockchain, federated learning, and regulatory technologies.

1. Australia’s National Science Agency
Emerging Technologies
in Data Sharing and
Analytics at Data61
Dr Liming Zhu, Research Director
Dr Mark Staples, Senior Principal Scientist
CSIRO’s Data61

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. Data61 Strategic Goals
Drive the development
and adoption of
Artificial Intelligence in
Australia, including
through our leadership
of a new National AI
Centre
1
Put digital science and
technology at the
heart of Australia’s
recovery and
resilience
2
Reinvent science,
using digital
technologies to
revolutionise the
future of scientific
discovery
3
3 |

4. Data61’s Strategic Focus Areas
• Market & Domains • Science & Technology
Industry 4.0/5.0
Defence
Environment and Natural Hazards
Digital Services
Digital Agriculture
AI and cybersecurity
Data in the Real World
AI for Science
Quantum technologies
Humans and machines
4 |

5. National AI Centre
• Lifting Australian businesses’ AI capability
• drive business adoption and the use of
transformative AI technologies to improve
productivity and lift competitiveness.
• Focus on SMEs & across multiple sectors
• Equipment/tools/research/training/links
• Data61 to lead/coordinate
• $24.7 million Next Generation Artificial
Intelligence Graduates Program
• $22.6 million Next Generation Emerging
Technology Graduates Program
• Data61 to manage the programs
$53.8 million National AI Centre
Also includes:
• AU Data Strategy
• Gov Data Sharing
• CDR
• Privacy Act
• AI Ethics
5 |

6. § More sources & types from public & partners
§ Intergovernmental data sharing
§ Access and use of sensitive data from another
organization/country
§ Privacy but also commercial and other sensitivity
§ Data analytics over encrypted data -
”sharing/use without access”
§ Open data/innovation (anonymized or
desensitized data)
Trend: Economic Value Arises from Data Sharing
& Joint Analytics
Data sharing, Data-as-a-service & AI/ML/Model-as-a-Service
6 |

7. Trend: Trust Shifts to Distributed Trust (Economy)
Local à Institutional à Distributed
Culture, external data, data
quality
7 |

8. Trend: Regulation/Ethic Overlay
Data Economy: Balancing Innovation & Regulation Burden
New Legislations
• EU’s GDPR: Privacy, security and “specific” purpose of use
• Australia
• Data Breach Notification Scheme
• Consumer Data Right (CDR): Open Banking, Energy..
• Payments system review
New Concerns
Balance between innovation and regulation
Ethical AI – Trust Data/AI-powered Service?
- Fairness, Accountability, Transparency, Privacy, Civil liberties…
- Rights to explanation and redress
8 |

9. Emerging Tech at Data61
9 |

10. 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
Sharing without Access - Federated Learning
From limited access to full encryption during use
10 |
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)

11. Analytics/Model to Data: Data Airlock
Not Data to Analytics/Model
• Model to Data; Insights back
• Enabled analytics of sensitive data
enabling flow of “insights” (not
data itself)
• Automated vetting of insights
• Case Studies: Major government
agency
11 |
Data is kept away in vaults.
All models and results are vetted.

12. Safe Data Sharing: Provable Privacy & Desensitization
Quantified risks assessment, mitigation and compliance
§ Provably private/desensitized data sharing/release for analytics collaboration
§ Quantified risks and mitigation
§ Expanding to other ethical principles and human values: fairness, transparency,
explainability…
§ Case Studies: Worked with 30+ Gov agencies
R4: Re-identification Risks Ready-Reckoner
12 |

13. Defend your model: Cybersecurity of AI/Models
Protecting your model (as well as your data) against adversarial AI
New threat vectors
§ Data poisoning
§ Adversarial examples
§ Model inversion/stealing through services
Solutions
§ Game theoretical approach
§ Immune system inspired
§ …
Case Studies:
13 |

14. Secure & Consent-Driven Data Sharing
Consider options to build in consent so customers can authorise data sharing
• Australia’s legislation impacting consumer data and its services
• Consumers can authorise 3rd parties to access their data
• Currently 3 designated sectors
• Data61’s Role (since 2018, recently transferred to Treasury)
• Setting Data API standards
• Security profiles standards ->
– Secure API analysis
– Critical infrastructure resilience & security
• Lessons in
• Sharing and analytics at scale
• Secure and consent-driven sharing https://consumerdatastandards.org.au
14 |

15. Data Linkages
(recommender systems,
knowledge graphs)
Data Management & Quality at Scale
Blue ones are Data61 Technologies
Data Quality Data Integrity
Metadata
Data
Ecosystem
Governance
Case Studies:
• data.gov.au (Data61’s https://magda.io/)
• COVID19 data sharing in PM&C
15 |
Process mining for
integrity, entity
resolution
Provenance / Lineage
Data
Discovery &
Visibility
Data Share
Technologies

16. Australia’s National Science Agency
Responsible AI
The Australian Approach

17. • 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
17 |
[1]

18. “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
18 |
[1]

19. Australia’s National Science Agency
There should be transparency and
responsible disclosure so people can
understand when they are being
significantly impacted by AI, and can
find out when an AI system is engaging
with them.
6. Transparency and
explainability

20. Explainability (& Interpretability) is Complex
•
Data61 work: R Hughes, C Edmond, L Wells, M Glencross, L Zhu, T Bednarz, eXplainable AI (XAI):
An introduction to the XAI landscape with practical examples. SigGraph Asia 2020
Interpretable by different stakeholders/users with
different interests and technical literacy
• AI experts, software developers/designers,
managers, boards, decision makers, users, affected
subjects, external auditor, regulator, public..
Properties of an Explanation (Miller 2019)
○ contrastive
■ i.e. in response to some counterfactual information (e.g. in
response to “why did X happen instead of Y?”)
○ selected
■ i.e. from a range of almost infinite causes, we select (in a
biased way) the most useful
○ refer to causes, not probabilities
○ social
■ i.e. presented as part of a conversation or interaction, in the
context of the beliefs of explainer and explained
20 |

21. Australia’s National Science Agency
People responsible for the different
phases of the AI system lifecycle should
be identifiable and accountable for the
outcomes of the AI systems, and human
oversight of AI systems should be
enabled.
8. Accountability

22. AI Governance is an Ecosystem Problem
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
22 |

23. Operationalising via Design 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)
23 |

24. Australia’s National Science Agency
Blockchain
(and Regtech)
Research perspectives at Data61

25. Blockchain

26. Blockchains
• Functionally, they are:
– A shared database (a ledger)
– A shared compute platform (“Smart contracts”)
• Logically-centralise data, but Decentralise control
• Blockchains are good for…
– New trustworthy and efficient ways to work together
– Exclusive control of digital assets
Centralised Trust
using a Third Party
Decentralised Trust
using a Blockchain
26 |

27. • Blockchain & DLT Standards
– ISO/TC 307
• Government Advisory
• Industry Projects & Technology
• Reports, Books
Blockchain at Data61
• Research
– Models and architecture for
blockchain-based systems
– Business process & blockchain
– Trustworthy blockchain
27 |

28. Making Money Smart

29. Commonwealth Bank of Australia | Confidential
Our NDIS proof of concept
29
Blockchain
NDIA Participant
Participant books
eligible services
using the app
Service
Provider
Conditions
checked
Agency
Manager
Plan
Manager
Carers /
Guardians
Service providers
receive smart tokens
for eligible services
Policy contracts
reflect budget
rules
Policy contracts can blend plan management approaches
New Payments
Platform
Service provider redeems
smart tokens for payment
NDIA facilitates data-rich
payments in near real-time
Smart
Tokens
Blockchain tokens
reflect plan
budgets
Pouch of
Tokens

30. • 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
Digital Finance CRC: Tokenisation
30 |

31. 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
31 |

32. Regtech

33. RegTech/ SupTech
Technology for dealing with regulation
• Functional Role
• Reporting
• Monitoring
• Verification
• Risk analysis
• Regulatory analysis
• Implementation and Governance
• Data Compliance management
• Addressing Risks
• AML/CTF/Sanctions Risk
• Conduct Risk
• Culture and Ethics Risk
• Data Protection Risk
• Domain Risk
• Fraud Risk
• Information Security Risk
• Process Risk
• Regulatory Compliance Risk
• Workforce Risk Management
33 |

34. • “Rules as Code” for automated compliance
• Text analytics to extract information from contracts, policies, …
• Machine Learning for transaction log analysis, graph analytics, …
• Cyberphysical monitoring of animals, fields, cargo
• Simulations of building performance using digital twins
Examples of Regtech at Data61
34 |

35. Collaborating with Data61/D61+ Network
Collaborative
innovation projects
with D61+ Network
(including 30+ Unis)
active
Tailored & scaled up
data fellow program
(bringing your
Agency’s challenges)
Trialling and
licensing the
enabling
technologies
Deep partnership via
shared technology
roadmaps
Culture via digital
transformation
lessons and
executive training
Contact:
Liming.Zhu@data61.csiro.au
Mark.Staples@data61.csiro.au
Wilma.James@data61.csiro.au
Pick a model & start today!
35 |
Thanks!

36. Australia’s National Science Agency
Appendix

37. 37 |

38. Trustworthy Blockchain-Based Systems?
• How do we design blockchain-
based systems that work?
• What is good evidence that they
will work?
– Functional correctness
– Non-Functional properties
• How do we get acceptance?
– Individual, Enterprise,
Regulatory, Societal
UI
IoT
Auxiliary
databases
Legacy
systems
Key
management
private
data
Blockchain is a
component
BIG
DATA

39. • DISER National Blockchain Roadmap, 1 Feb 2020
– Working groups on Supply chain; Credentials; Regtech; Cybersecurity
– Launched Australian Public Service Blockchain Network
– Announced $6.9M for 2 pilot projects, to deliver 2021-22
• RBA wholesale CBDC pilot, Nov 2020
• Australian Border Force Inter-Government Ledger
– Trial with Singapore on Certificates of Origin, Nov 2020
• IP Australia, Smart Trade Mark system since 2018, trials underway
• AusTrade Blockchain missions and events since 2017
• Support for Standards Australia, leading ISO/TC 307 since 2016
• Engaged & proactive regulators including ASIC, AUSTRAC, ATO, Treasury
Blockchain in Australian Government
39 |

40. • Australian Stock Exchange (ASX) CHESS replacement project
– Clearance and settlement system; project from 2016 (planned live in 2023)
• Bond-i: world’s 1st whole-of-lifecycle bond on blockchain
– Commonwealth Bank & World Bank, August 2018
• Lygon: commercial property bank guarantees, live since Feb 2021
• Many other companies in energy, agriculture, trade, mining, …
• Active industry body Blockchain Australia
• Digital Finance CRC, starting Jan 2022
Blockchain in Australian Industry
40 |