Title: Strategic Interplay: Game-Theoretic Frameworks for Topological Robustness Against Data Poisoning
Abstract: This investigation explores the strategic dynamics between adversarial
manipulation and defensive mechanisms through the lens of game
theory and topological data analysis. We construct a novel theoretical
framework that synthesizes concepts from cooperative game theory with
the structural insights provided by persistence homology to formulate
defensive strategies against data poisoning attacks. Our central contribution
is a game-theoretic equilibrium model that characterizes the
competitive interaction between attackers attempting to compromise
data integrity and defenders working to preserve topological invariants.
We introduce the concept of topological resilience coefficient as a
measure of structural vulnerability, supported by a novel theorem establishing
bounds on attack effectiveness under equilibrium conditions.
Experimental validation demonstrates that our approach yields significantly
improved robustness against sophisticated poisoning strategies
when compared to conventional defenses. The presented framework
offers both theoretical foundations and practical methodologies for designing
systems resistant to adversarial manipulation while preserving
essential structural characteristics in machine learning applications.
Bio: To be announced soon