This volume contains the proceedings of ASIACRYPT '91, the first international conference on the theory and application of cryptology to be held in the Asian area. It was held at Fujiyoshida, near Mount Fuji in Japan, in November 1991. The conference was modeled after the very successful CRYTO and EUROCRYPT series of conferences sponsored by the International Association for Cryptologic Research (IACR). The IACR and the Institute of Electronics, Information and Communication Engineers were sponsors for ASIACRYPT '91. The papers from the conference were improved and corrected for inclusion in this volume. The papers are grouped into parts on: differential cryptanalysis and DES-like cryptosystems; hashing and signature schemes; secret sharing, threshold, and authenticationcodes; block ciphers - foundations and analysis; cryptanalysis and new ciphers; proof systems and interactive protocols; public key ciphers - foundations and analysis. Also included are four invited lectures and impromptu talks from the rump session.

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.