Anonymisation is for Everyone
Helping you bring a structured approach to anonymisation
There has been an explosion in the collection, usage and sharing of personal data. Regulations protecting personal and personally-identifiable data mean that both individuals and companies are increasingly reluctant to share data that could bring huge benefits to everyone.
Could anonymisation provide the answer and how can you begin to apply it to data?
This course will introduce the latest thinking on anonymisation and work through a practical exercise in anonymising data while maintaining utility and minimising risk. Through the practical exercise, we will explore the needs of different stakeholders, both internal and external and explore how tools like the ODI Data Ethics Canvas and Consequence Scanning toolkit can help.
£85.00 + VAT
£90.00 + VAT
£100.00 + VAT
To ensure inclusivity, we offer discounted pricing for customers from lower or middle-income countries.
Please contact us for further information.
If you represent a business interested in purchasing four or more course spaces, please reach out to us for personalised discounted rates.
More about the course
Who is this course for?
This course aims to help decision-makers such as compliance professionals, technical managers, product owners, and service managers bring a structured approach to anonymisation to the projects they are leading.
Course prerequisites
This course is designed to meet the needs of decision-makers. Hence, no prerequisites required other than the desire to learn!
Learning experience and course delivery
The aim of this course is to help those who want to make (potentially re-identifiable) data more accessible – and to do so in a way that minimises the risks of re-identification, protects citizens’ privacy, and increases the social and economic value created from that data.
By the end of this course you will be able to:
- Describe what anonymisation is and its benefits;
- Evaluate when anonymisation needs to be applied and when does it not;
- Explain a number of case-studies in data anonymisation;
- Identify different anonymisation techniques in practice;
- Describe key aspects of current regulatory frameworks concerning anonymisation;
- Apply a number of anonymisation techniques to data;
- Analyse the utility-risk trade-off when sharing data with stakeholders;
- Use a number of practical tools to help maximise data utility while minimising risk;
Course cost
ODI members: £85 (+VAT where applicable) (from 18.09.23)
General admission: £100 (+VAT where applicable.)
Not an ODI member?
You can join as an individual for £100.
Organisations can join and share the training discount across their entire business. The cost for organisations is between £360 and £2,200, depending on sector and size.
Click here to find out more about ODI membership.
Running this course with your team
This course can be run for groups of up to 18 people. And, we can tailor our content to your context and needs.
We can work with you to identify the data skills required across your business, creating training that meets your organisation’s needs.
Contact our Data Skills Experts to discuss how we can improve your organisations data literacy.
FAQs
How many places/people are on each course?
Each course date will consist of up to 18 participants.
Is there a limit to the number of people from my company that can attend?
Yes, there is a maximum of 3 places per organisation. If you are interested in booking more than 3 places for your organisation, please contact training@theodi.org.
About this course
This course was created by and is © The Open Data Institute. It was developed with the help of funding from the Erasmus+ programme of the European Union under the Open Data Education project.
What you'll learn
Course instructors
The Data Skills Framework
The ODI Skills Framework provides a balanced view of the skills an organisation needs in order to build an open, trustworthy data ecosystem, and manage harmful impacts.
Digital Analyst, Boots
"The assignments were the highlight of the course. They were well designed – it’s good to use real live data from a different sphere than I’m used to working in. And I really liked how they built on the previous assignment learnings.”
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