Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
Explore advanced techniques for implementing differential privacy in graph cut problems through this technical conference talk that addresses the minimum k-cut and multiway cut challenges. Learn about edge-differentially private algorithms that achieve nearly optimal performance while maintaining privacy guarantees. Discover how to develop private algorithms for multiway cut problems with multiplicative approximation ratios matching state-of-the-art non-private approaches. Examine tight information-theoretic lower bounds on additive error and understand why these algorithms are near-optimal for weighted graphs with constant k. Master the application of known bounds on approximate k-cuts to create private algorithms with optimal additive error O(k log n) for fixed privacy parameters. Analyze information-theoretic lower bounds that match additive error requirements and explore efficient private algorithms for non-constant k scenarios. Gain insights into polynomial-time 2-approximation methods with additive error of Õ(k^1.5) for practical implementation in privacy-preserving graph analysis applications.
