Traditional steganography method often hides secret data by establishing a mapping relationship between secret data and a cover image or directly in a noisy area, but has a low embedding capacity. Based on the thought of deep learning, in this work, we propose a new image steganography scheme based on a U-Net structure. First, in the form of paired training, the trained deep neural network includes a hiding network and an extraction network; then, the sender uses the hiding network to embed the secret image into another full-size image without any modification and send it to the receiver. Finally, the receiver uses the extraction network to reconstruct secret image and original cover image correctly. Experimental results show that the proposed scheme compresses and distributes the information of embedded secret image into all available bits in the cover image, which not only solves the obvious visual cues problem, but also increases the embedding capacity.

Threshold private set intersection (TPSI) allows multiple parties to learn the intersection of their input sets only if the size of the intersection is greater than a certain threshold. This task has been demonstrated useful with practical applications, and thus many active research has been conducted. However, current solutions for TPSI are still slow for large input sets e.g., n=2^20 for the set size, and the potentially practical candidates are only secure against semi-honest adversaries. For the basic PSI, there have been efficient and scalable solutions, even in the malicious settings. It is interesting to determine whether adding a threshold feature would inherently incur a large overhead to PSI. To bridge the gap, we introduce a new notion called probabilistic TPSI, where the parties learn the intersection \I with probability proportional to the size of \I. This functionality trades a small probability of bad events (i.e., the parties can learn insufficiently large \I) for new directions of efficient and scalable designs. As a novel technical contribution, we design an efficient multiparty probabilistic set size test protocol, which together with any standard PSI realizes the probabilistic variant in the semi-honest setting. Even though this generic approach cannot be generalized to the malicious setting, we identify nice properties from the prior OPRF-based PSI, which can be further blended with our probabilistic set size test to achieve a probabilistic TPSI protocol against malicious adversaries, with competitive efficiency and scalability. In summary, we show (1) in the semi-honest setting, there exists a probabilistic TPSI that is essentially as efficient as a generic PSI, and (2) in the malicious setting, there is a two-party probabilistic TPSI that is essentially 2x the OPRF-based PSI of the work (Raghuraman and Rindal, CCS 2022), implying a small gap between PSI and the probabilistic TPSI. We conduct comprehensive experiments to evaluate concrete efficiency of our individual building blocks and the overall protocol. Particularly, our overall two-party protocol for the probabilistic TPSI runs within 7 or 10 seconds (online) for n=2^20 in the semi-honest and malicious settings, respectively. This confirms the practical advantages of our approach.

Threshold private set intersection (TPSI) allows a receiver to obtain the intersection when the cardinality of the intersection is greater or equal to the threshold, which has a wide range of applications such as fingerprint matching, online dating and ridesharing. Existing TPSI protocols are inefficient because almost all of them rely on lots of expensive public-key techniques or require an exponential number of possible combinations among the shares. In this work, we design an efficient TPSI protocol, which achieves computational security in semi-honest model. To improve the efficiency of the TPSI protocol, we design a new TPSI protocol based on garbled Bloom filter (GBF) and threshold secret sharing, which uses a small amount of public-key operations. Moreover, our protocol combines with the Reed-Solomon decoding algorithm to reconstruct the secret which is a feasible method to avoid calculating all possible combinations among the shares. The performance analysis shows that our protocol is more efficient than the previous TPSI protocols. To the best of our knowledge, the optimal TPSI protocol implemented by Zhao and Chow (WPES'18) has an online time of 78 seconds to compute the intersection of two datasets of 100 elements each with threshold t = 50. In contrast, our protocol has a total time of 2.988 seconds.

Secret sharing is an important component of cryptography protocols and has a wide range of practical applications. However, the existing secret sharing schemes cannot apply to computationally weak devices and cannot efficiently guarantee fairness. In this study, a novel outsourcing secret sharing scheme is proposed. In the setting of outsourcing secret sharing, clients only need a small amount of decryption and verification operations, while the expensive reconstruction computation and verifiable computation can be outsourced to cloud service providers (CSP). The scheme does not require complex interactive argument or zero‐knowledge proof. The malicious behaviour of clients and CSP can be detected in time. Moreover, the CSP cannot get any useful information about the secret, and it is fair for every client to obtain the secret. At the end of this study, the authors prove the security of the proposed scheme and compare it with other secret sharing schemes.