Machine unlearning has garnered widespread attention, due to various reasons, including privacy-preserving, model usability, and legal regulations. It requires model providers to unlearning users' data from models upon receiving unlearning request. Recommendation systems have also been extensively researched in the field of deep learning, particularly within the context of big data environments. However, little research can be found to verify the effectiveness of unlearning approach using pure tabular data-based recommendation scenario. In this paper, we propose a recommendation unlearning verification (RUV) scheme based on non-influential trigger data, which fills this gap. Users can use the recommendation rate for selected target items to determine whether the recommendation system complies with unlearning requests. Evaluation results on real datasets confirm the efficiency and effectiveness of our proposed RUV scheme.

While deep learning-based recommendation systems have achieved great success, recommendation system models are also at serious risk of intellectual property infringement. Current model watermarking research faces significant challenges in terms of fidelity, invisibility, and efficiency. Additionally, existing model watermarking techniques are predominantly applied to image data, with limited applicability to tabular data. In this paper, we introduce an innovative watermarking framework designed to safeguard the ownership of recommendation system models. Specifically, we verify recommendation system model ownership by embedding a type of backdoor watermark into the training dataset, which does not affect model performance. We have conducted experiments on several classical datasets to validate the reliability and effectiveness of our approach.

Deep learning is being extensively utilized across various domains, with deep learning-based recommendation systems gaining prominence due to their exceptional performance. However, these systems are vulnerable to data poisoning attacks, where adversaries introduce carefully crafted fake user ratings to compromise the integrity of the recommendation model. We propose a dual defense to address this threat. The first line of defense, termed active defense, preemptively reduces the system’s vulnerability to poisoning attacks by incorporating crafted regularization into the loss function. This approach diminishes the attacker’s impact while preserving system performance, thereby lowering the success rate of targeted attacks. To further enhance the system’s robustness, we introduce a Generative Adversarial Network (GAN) based detection model as a passive defense strategy to accurately identify and filter out poisoned data. Empirical evaluations on three distinct datasets demonstrate that our dual defense approach significantly enhances both the proactive defense and passive detection capabilities of recommendation systems, effectively countering data poisoning attacks.

During the deployment and utilization of federated models, they are susceptible to unauthorized theft or misuse. To address this issue, researchers have proposed the use of watermarking techniques to protect the Intellectual Property (IP) of the federated models. Nevertheless, traditional watermarking methods in federated learning have certain limitations. It is highly likely that different clients may embed watermarks in the same region of the model. During the aggregation of the watermarked weights, the watermarks from various clients may overlap, resulting in conflicts between the embedded watermarks. To overcome these challenges, we propose a novel method called Non-Interfering Fragmented Watermarking for Federated Models (FedNIFW). In the proposed scheme, each client node is assigned a specific segment of the neural network layer where watermarking can be applied. During training, each client is allowed to embed watermarks only within their designated segments, while other segments intended for watermarking by different clients are frozen. Experimental results demonstrate that this segmented watermarking scheme effectively prevents conflicts between client watermarks and does not significantly impact the accuracy of the federated models. These findings underscore the feasibility of the proposed watermarking scheme.

Smart home is an emerging form of the Internet of Things (IoT), enabling people to enjoy a convenient and intelligent life. The data generated by smart home devices are transmitted through the public channel, which is not secure enough, so the secret data in smart home are easily intercepted by malicious adversaries. In order to solve this problem, this paper proposes a smart home privacy protection method combining DES encryption and the improved Least Significant Bit (LSB) information hiding algorithm, changing the practice of directly exposing smart home secret information to the Internet, first, using Data Encryption Standard (DES) encryption to encrypt the smart home information and second, the improved LSB information hiding algorithm is used to hide the ciphertext, so that the adversary cannot detect the smart home secret information. The goal of the scheme is to provide a double protection for the secure transmission of the smart home secret information. If an attacker wants to carry out an attack, it has to break through at least two defense lines, which seems impossible to do. Experiment results show that the improved LSB algorithm is more robust than the existing algorithms, and it is very safe. Therefore, the scheme proposed in this paper is very practical for protecting the smart home secret information.