Sjt risks and mitigations of releasing data
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This document discusses risks and mitigations when releasing data. It defines risk as the probability of something happening multiplied by the resulting cost or benefit. There are risks of physical harm, legal harm, reputational harm, and privacy breaches to data subjects, collectors, processors, and those releasing the data. Risk levels can be low, medium, or high. The document provides strategies for mitigating risks such as considering partial data releases, including locals to assess risks in local languages/contexts, and being aware of how data may interact with other datasets. It also discusses handling personally identifiable information by learning to spot red flags like names, addresses, exact locations, codes, and uncommon terms that could identify individuals.
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**Introduction**
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**Historical Background and Development**
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2. **Pretraining:** The model learns general language patterns through unsupervised learning, predicting missing words or the next sequence in a text.
3. **Fine-tuning:** Adjusting the model for specific tasks, such as summarization, translation, or question-answering, using supervised learning.
4. **Inference:** The trained model generates text based on user input, leveraging probabilistic predictions to produce coherent responses.
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**Applications of LLMs**
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https://aaai.org/conference/aaai/aaai-25/workshop-list/#ws14Sjt risks and mitigations of releasing data
- 1. Risks and mitigations of releasing data Risk analysis and complexity in de-identifying and releasing data. Sara-Jayne Terp RDF Discussion
- 2. First, Do No Harm If you make a dataset public, you have a responsibility, to the best of your knowledge, skills, and advice, to do no harm to the people connected to that dataset. You balance making data available to people who can do good with it and protecting the data subjects, sources, and managers. 2
- 3. What is risk? What is the risk here? 3
- 4. RISK The probability of something happening multiplied by the resulting cost or benefit if it does (Oxford English Dictionary) Three parts: Cost/benefit Probability Subject (to what/whom) 4
- 5. Subjects: Physical 5 Witnesses told us that a helicopter had been circling around the area for hours by the time the bakery opened in the afternoon. It had, perhaps, 200 people lined up to get bread. Suddenly, the helicopter dropped a bomb that hit a building
- 10. Risk OF What? Physical harm Legal harm (e.g. jail, IP disputes) Reputational harm Privacy breach 10
- 11. Risk to Whom? Data subjects (elections example) Data collectors (conflict example) Data processing team (military equipment example) Person releasing the data (corruption example) Person using the data 11
- 13. piI How I handle it 13
- 14. PII Personally identifiable information油(PII) is any data that could potentially identify a specific individual. Any information that can be used to distinguish one person from another and can be used for de- anonymizing anonymous data can be considered油PII. 14
- 15. Learn to spot Red Flags Names, addresses, phone numbers Locations: lat/long, GIS traces, locality (e.g. home + work as an identifier) Members of small populations Untranslated text Codes (e.g. 41) Slang terms Can be combined with other datasets to produce PII 15
- 16. Consider Partial Release Release to only some groups Academics People in your organisation Data subjects Release at lower granularity Town/district level, not street Subset or sample of data rows Subset of data columns 16
- 17. Include locals Locals can spot: Local languages Local slang Innocent-looking phrases Locals might also choose the risk 17
- 18. Consider Interactions Between Datasets 18
- 19. Learn From Experts Over to you 19
- 20. THANK YOU For questions or suggestions: Responsible Data Forum For questions or suggestions: Responsible Data Forum