Privacy-Preserving Computing: Use Data Without Exposing It
Privacy-Preserving & Confidential Computing: leave with a differentially private statistics release with a privacy-budget and accuracy trade-off analysis.
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- Bring your own work — code, campaign, report or plan
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why privacy-preserving & confidential computing matters — the common problem: Teams must choose between using sensitive data and protecting it.
- 2Core concepts in plain language: Differential privacy, federated learning, secure aggregation, secure multiparty computation, homomorphic encryption, trusted execution environments
- 3Going further: confidential computing, privacy-preserving analytics, data-sharing controls
- 4Framework walkthrough: Amdahl's Law, Threat Models for Decentralized Systems, Privacy Budgets (ε)
- 5Practical workflow, built live: A differentially private statistics release with a privacy-budget and accuracy trade-off analysis.
- 6How to measure it: Speed-up versus baseline, Numerical error, Security review findings
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A differentially private statistics release with a privacy-budget and accuracy trade-off analysis.
- A working understanding of Amdahl's Law and Threat Models for Decentralized Systems
- A short list of measures to track: Speed-up versus baseline, Numerical error, Security review findings
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