Combinatorial Characterizations of Algebraic Manipulation Detection Codes Involving Generalized Difference Families
This work addresses theoretical security problems in coding theory, but it appears incremental as it builds on existing AMD code frameworks with combinatorial extensions.
This paper mathematically analyzes optimal algebraic manipulation detection (AMD) codes, proving lower bounds on adversary success probability and providing combinatorial characterizations of codes that achieve these bounds using generalized difference families.
This paper provides a mathematical analysis of optimal algebraic manipulation detection (AMD) codes. We prove several lower bounds on the success probability of an adversary and we then give some combinatorial characterizations of AMD codes that meet the bounds with equality. These characterizations involve various types of generalized difference families. Constructing these difference families is an interesting problem in its own right.