Differentially Private Multiway and k-Cut

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.