The Hardness of Learning Access Control Policies

The problem of learning access control policies is gaining significant attention in research. We contribute to the foundations of this problem by posing and addressing meaningful questions on computational hardness. Our study focuses on learning access control policies within three different models: the access matrix, Role-Based Access Control (RBAC), and Relationship-Based Access Control (ReBAC), as described in existing literature. Our approach builds upon the well-established concept of Probably Approximately Correct (PAC) theory, with careful adaptations for our specific context. In our setup, the learning algorithm receives data or examples associated with access enforcement, which involves deciding whether an access request for resource should be accepted or denied. For the access matrix, we pose a learning problem that turns out to be computationally easy, and another that we prove is computationally hard. We generalize the former result so we have a sufficient condition for establishing other problems to be computationally easy. Building upon these findings, we examine five learning problems in the context of RBAC, of which three are identified as computationally easy and two are proven to be computationally hard. Finally, we consider four learning problems in the context of ReBAC, all of which are found to be computationally easy. Every proof for a problem that is computationally easy is constructive, in that we propose a learning algorithm for the problem that is efficient, and probably, approximately correct. As such, our work makes contributions at the foundations of an important, emerging aspect of access control, and thereby, information security

Teacher Efficacy, Work Engagement, and Social Support Among Chinese Special Education School Teachers

This paper investigates the relationship between teacher efficacy and sociodemographic factors, work engagement, and social support among Chinese special education school teachers. The sample comprised 1,027 special education school teachers in mainland China. The Teachers’ Sense of Efficacy Scale, the Multi-Dimensional Scale of Perceived Social Support, and the Utrecht Work Engagement Scale were used for data collection. Correlation analysis revealed that social support, work engagement, and teacher efficacy were significantly correlated with each other. Additionally, gender, years of experience, and monthly salary were significant predictors of teacher efficacy. Furthermore, structural equation modeling analysis showed that social support exerted its indirect effect on teacher efficacy through the mediation of work engagement. The findings of this study provide a new perspective on the complex association between social support and teacher efficacy. The explanations and limitations of these findings are discussed

Learning Personalized Story Evaluation

While large language models (LLMs) have shown impressive results for more objective tasks such as QA and retrieval, it remains nontrivial to evaluate their performance on open-ended text generation for reasons including (1) data contamination; (2) multi-dimensional evaluation criteria; and (3) subjectiveness stemming from reviewers' personal preferences. To address such issues, we propose to model personalization in an uncontaminated open-ended generation assessment. We create two new datasets Per-MPST and Per-DOC for personalized story evaluation, by re-purposing existing datasets with proper anonymization and new personalized labels. We further develop a personalized story evaluation model PERSE to infer reviewer preferences and provide a personalized evaluation. Specifically, given a few exemplary reviews from a particular reviewer, PERSE predicts either a detailed review or fine-grained comparison in several aspects (such as interestingness and surprise) for that reviewer on a new text input. Experimental results show that PERSE outperforms GPT-4 by 15.8% on Kendall correlation of story ratings, and by 13.7% on pairwise preference prediction accuracy. Both datasets and code will be released.Comment: 19 page