Zhaoyang Han

In the era of big data, it is essential to securely and efficiently combine the large amounts of private data owned by different companies or organizations to make correct decisions. Secure Multiparty Computation (SMPC) works as a general cryptographic primitive, which enables distributed parties to collaboratively compute an arbitrary functionality without revealing their own private inputs. While SMPC may potentially address this task, several issues, such as computation efficiency and correctness validation, have to be overcome for practical realizations. To tackle these issues, we designed a secure and efficient decision-making scheme to enable clients to outsource data and computations to cloud servers while ensuring the integrity and confidentiality of the input and output, in addition to the correctness of the results. Moreover, we implemented our scheme based on an SMPC computation framework named MP-SPDZ. The experimental evaluation results showed that our proposed scheme is feasible and efficient for practical realizations.

Vertical federated learning is designed to protect user privacy by building local models over disparate datasets and transferring intermediate parameters without directly revealing the underlying data. However, the intermediate parameters uploaded by participants may memorize information about the training data. With the recent legislation on the“right to be forgotten”, it is crucial for vertical federated learning systems to have the ability to forget or remove previous training information of any client. For the first time, this work fills in this research gap by proposing a vertical federated unlearning method on logistic regression model. The proposed method is achieved by imposing constraints on intermediate parameters during the training process and then subtracting target client updates from the global model. The proposed method boasts the advantages that it does not need any new clients for training and requires only one extra round of updates to recover the performance of the previous model. Moreover, data-poisoning attacks are introduced to evaluate the effectiveness of the unlearning process. The effectiveness of the method is demonstrated through experiments conducted on four benchmark datasets. Compared to the conventional unlearning by retraining from scratch, the proposed unlearning method has a negligible decrease in accuracy but can improve training efficiency by over 400%.

IoT devices are now enriching people’s life. However, the security of IoT devices seldom attracts manufacturers’ attention. There are already some solutions to the problem of connecting a smart device to a user’s wireless network based on the 802.11 transmission such as Smart Config from TI. However, it is insecure in many situations, and it does not have a satisfactory transmission speed, which does not mean that it has a low bit rate. It usually takes a long time for the device to recognize the data it receives and decode them. In this paper, we propose a new Wi-Fi connection method based on audio waves. This method is based on MFSK (Multiple frequency-shift keying) and works well in short distance, which enables the correctness and efficiency. In addition, audio waves can hardly be eavesdropped, which provides higher security than other methods. We also put forward an encryption solution by using jamming signal, which can greatly improve the security of the transmission.