With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. However, the training process still requires vast computations, and therefore cannot be accomplished by some resource-constrained devices. To solve this problem, the resource-constrained device can outsource the BLS algorithm to cloud servers. Nevertheless, some security challenges also follow with the use of cloud computing, including the privacy of the data and the correctness of returned results. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm not only improves the efficiency of the algorithm on the client but also ensures that the clients sensitive information is not leaked to the cloud server. In addition, in our algorithm, the client can verify the correctness of returned results with a probability of almost 1. Finally, we analyze the security and efficiency of our algorithm in theory and prove our algorithms feasibility through experiments.

翻译：随着人工智能的迅速发展以及5G时代的到来,深层次的学习得到了研究人员的广泛关注。宽广的学习系统(BLS)是最近提出的一个新的深层次学习模式,它展示了它在图像识别和发现错误等许多领域的有效性。然而,培训过程仍然需要大量计算,因此无法用一些资源限制的装置完成。解决这个问题,资源限制的装置可以将BLS算法外包给云服务器。然而,在使用云计算时也随之出现一些安全挑战,包括数据的隐私和返回结果的正确性。在本文中,我们建议为BLS提出一种安全、高效和可核查的外包算法。这种算法不仅提高了客户算法的效率,而且还确保客户敏感信息不会泄漏到云服务器。此外,在我们算法中,客户可以核实返回结果的正确性,几乎1。最后,我们分析我们的算法在理论上的安全和效率,并通过实验证明我们的算法的可行性。