With the widespread application of large language models (LLMs), user privacy protection has become a significant research topic. Existing privacy preference modeling methods often rely on large-scale user data, making effective privacy preference analysis challenging in data-limited environments. This study explores how LLMs can analyze user behavior related to privacy protection in scenarios with limited data and proposes a method that integrates Few-shot Learning and Privacy Computing to model user privacy preferences. The research utilizes anonymized user privacy settings data, survey responses, and simulated data, comparing the performance of traditional modeling approaches with LLM-based methods. Experimental results demonstrate that, even with limited data, LLMs significantly improve the accuracy of privacy preference modeling. Additionally, incorporating Differential Privacy and Federated Learning further reduces the risk of user data exposure. The findings provide new insights into the application of LLMs in privacy protection and offer theoretical support for advancing privacy computing and user behavior analysis.

Mining tasks over sequential data, such as clickstreams and gene sequences, require a careful design of embeddings usable by learning algorithms. Recent research in feature learning has been extended to sequential data, where each instance consists of a sequence of heterogeneous items with a variable length. However, many real-world applications often involve attributed sequences, where each instance is composed of both a sequence of categorical items and a set of attributes. In this paper, we study this new problem of attributed sequence embedding, where the goal is to learn the representations of attributed sequences in an unsupervised fashion. This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences. This problem is challenging due to the dependencies between sequences and their associated attributes. We propose a deep multimodal learning framework, called NAS, to produce embeddings of attributed sequences. The embeddings are task independent and can be used on various mining tasks of attributed sequences. We demonstrate the effectiveness of our embeddings of attributed sequences in various unsupervised learning tasks on real-world datasets.

Ed. note: This is the first article in a two-part series about AI, its potential impact on how organizations approach security, and the accompanying considerations around implementation, efficacy, and compliance. Is Artificial Intelligence (AI) on track to help the world streamline and solve against tasks that are better left to a machine? One might think so, given everything we've seen and heard about the impact of AI on our society -- from our phones telling us the best way to drive home, to chatbots on e-commerce sites answering product questions, to devices as small as a thermostat or as large as an electric vehicle removing friction from everyday life. Now AI is entering the space of cybersecurity, promising to bring greater speed and accuracy in detecting and responding to breaches, user behavior analysis, or predicting new strains of malware. AI and machine learning technologies can help protect organizations from a continuously evolving threat landscape -- but AI is not just for sophisticated attacks, AI can also help protect against classic attack scenarios.

Ed. note: This is the first article in a two-part series about AI, its potential impact on how organizations approach security, and the accompanying considerations around implementation, efficacy, and compliance. Is Artificial Intelligence (AI) on track to help the world streamline and solve against tasks that are better left to a machine? One might think so, given everything we've seen and heard about the impact of AI on our society -- from our phones telling us the best way to drive home, to chatbots on e-commerce sites answering product questions, to devices as small as a thermostat or as large as an electric vehicle removing friction from everyday life. Now AI is entering the space of cybersecurity, promising to bring greater speed and accuracy in detecting and responding to breaches, user behavior analysis, or predicting new strains of malware. AI and machine learning technologies can help protect organizations from a continuously evolving threat landscape -- but AI is not just for sophisticated attacks, AI can also help protect against classic attack scenarios.