Machine learning models have become integral to various aspects of daily life, prompting increased vulnerability to adversarial attacks.Adversarial training is one of the most promising and practical methods to enhance model robustness.Existing adversarial training methods, however, assume access to the original training data.But nowadays, more and more users directly download models from the open-source model platforms or tech companies, but the original training datasets are usually unreleased because of commercial interests or privacy.In such scenarios, the user cannot utilize the former adversarial training methods to improve model robustness because of the lack of original training datasets.Thus, we present the first exploration of a data-free adversarial training framework, Alchemy, which seeks to enhance model robustness without requiring access to the original training data.By addressing the notable challenges of reconstructing high-quality training data with robust features and improving the adversarial robustness to the inaccessible original dataset, our approach achieves the goals of both high accuracy maintenance and robustness improvement.Comprehensive experiments on four datasets compared with five baselines, demonstrate Alchemy 's high effectiveness.With no access to any training dataset, the average robustness improvement with Alchemy is effective in most attack scenarios.Additional evaluations underscore the framework's stability under different settings and discuss future research directions.

Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects' answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier than LIST and CAT.