Homomorphic Encryption (HE) allows computations on encrypted data without decryption. It can be used where the users' information are to be processed by an untrustful server, and has been a popular choice in privacy-preserving applications. However, in order to obtain meaningful results, we have to assume an honest-but-curious server, i.e., it will faithfully follow what was asked to do. If the server is malicious, there is no guarantee that the computed result is correct. The notion of verifiable HE (vHE) is introduced to detect malicious server's behaviors, but current vHE schemes are either more than four orders of magnitude slower than the underlying HE operations (Atapoor et. al, CIC 2024) or fast but incompatible with server-side private inputs (Chatel et. al, CCS 2024). In this work, we propose a vHE framework ZHE: efficient Zero-Knowledge Proofs (ZKPs) that prove the correct execution of HE evaluations while protecting the server's private inputs. More precisely, we first design two new highly-efficient ZKPs for modulo operations and (Inverse) Number Theoretic Transforms (NTTs), two of the basic operations of HE evaluations. Then we build a customized ZKP for HE evaluations, which is scalable, enjoys a fast prover time and has a non-interactive online phase. Our ZKP is applicable to all Ring-LWE based HE schemes, such as BGV and CKKS. Finally, we implement our protocols for both BGV and CKKS and conduct extensive experiments on various HE workloads. Compared to the state-of-the-art works, both of our prover time and verifier time are improved; especially, our prover cost is only roughly 27–36× more expensive than the underlying HE operations, this is two to three orders of magnitude cheaper than state-of-the-arts.

Data‐hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data‐hunger in machine‐learning‐based image‐processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data‐hunger. Based on these findings, this paper introduces a novel semi‐white‐box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single all‐in‐one model, a semi‐white‐box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well‐established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi‐white‐box strategy over the traditional all‐in‐one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi‐white‐box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semi‐white‐box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the all‐in‐one approach. In conclusion, this paper contributes to mitigating data‐hunger challenges in machine‐learning‐based image processing through the introduction of a novel semi‐white‐box model construction strategy, backed by empirical evidence of its effectiveness.

Recent works on interactive zero-knowledge (ZK) protocols provide a new paradigm with high efficiency and scalability. However, these protocols suffer from high communication overhead, often linear to the circuit size. In this paper, we proposed two new ZK protocols with communication sublinear to the circuit size, while maintaining a similar level of computational efficiency.

Kleptography introduced by Young and Yung is about using an embedded backdoor to perform attacks on a cryptosystems. At SAC'17, Kwantet al. proposed a kleptographic backdoor on NTRU encryption scheme and thought that the backdoor can not be detected. However, in this paper we show that the user can detect the backdoor very efficiently and hence the problem of constructing a kleptographic backdoor on NTRU stays open. Moreover, we also design a universal method to embed a kleptographic backdoor for RLWE-based scheme, such as NewHope. Our construction is shown to be strongly undetectable, which reveals the threats of the kleptographic attacks on lattice-based schemes.