A large amount of valuable information resides in a decentralized attributed social graph, where each user locally maintains a limited view of the graph. However, there exists a conflicting requirement between publishing an attributed social graph and protecting the privacy of sensitive information contained in each user's local data. In this paper, we aim to collect and generate attributed social graphs in a decentralized manner while providing local differential privacy (LDP) for the collected data. Existing LDP-based synthetic graph generation methods either fail to preserve important graph properties (such as modularity and clustering coefficient) due to excessive noise injection or are unable to process attribute data, thus limiting their adoption and applicability. To overcome these weaknesses, we propose AsgLDP, a novel technique to generate privacy-preserving attributed graph data while satisfying LDP. AsgLDP preserves various graph properties through carefully designing the injected noise and estimating the joint distribution of attribute data. There are two key steps in AsgLDP: 1) collecting and generating graph data while satisfying LDP, and 2) optimizing the privacy-utility tradeoff of the generated data while preserving general graph properties such as the degree distribution, community structure and attribute distribution. Through theoretical analysis as well as experiments over 6 real-world datasets, we demonstrate the effectiveness of AsgLDP in preserving general graph properties such as degree distribution, community structure and attributed community search, while rigorously satisfying LDP. We also show that AsgLDP achieves a superior balance between utility and privacy as compared to the state-of-the-art approaches.

Zero-knowledge proof is a critical cryptographic primitive. Its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been deployed in various privacy-preserving applications such as cryptocurrencies and verifiable machine learning. Unfortunately, zkSNARK like Groth16 has a high overhead on its proof generation step, which consists of several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and multi-scalar multiplication (MSM). Therefore, this paper presents cuZK, an efficient GPU implementation of zkSNARK with the following three techniques to achieve high performance. First, we propose a new parallel MSM algorithm. This MSM algorithm achieves nearly perfect linear speedup over the Pippenger algorithm, a well-known serial MSM algorithm. Second, we parallelize the MUL operation. Along with our self-designed MSM scheme and well-studied NTT scheme, cuZK achieves the parallelization of all operations in the proof generation step. Third, cuZK reduces the latency overhead caused by CPU-GPU data transfer by 1) reducing redundant data transfer and 2) overlapping data transfer and device computation. The evaluation results show that our MSM module provides over 2.08x (up to 2.94x) speedup versus the state-of-the-art GPU implementation. cuZK achieves over 2.65x (up to 4.86x) speedup on standard benchmarks and 2.18× speedup on a GPU-accelerated cryptocurrency application, Filecoin.

In recent years, DeepFake technologies have seen widespread adoption in various domains, including entertainment and film production. However, they have also been maliciously employed for disseminating false information and engaging in video fraud. Existing detection methods often experience significant performance degradation when confronted with unknown forgeries or exhibit limitations when dealing with low-quality images. To address this challenge, we introduce Diff-ID , a novel approach designed to elucidate and quantify the identity loss induced by facial manipulations. When assessing the authenticity of an image, Diff-ID leverages a genuine image of the same individual as a reference and processes two images jointly. It aligns the reference image and the test image into the same identity-insensitive attribute feature space using a face-swapping generator. This alignment allows us to observe the identity disparities between the two images through the differences in the aligned generation pairs. Subsequently, we have developed a custom metric designed to quantify the identity loss relative to the reference image in the test image. This metric effectively distinguishes forgery images from the real ones. Extensive experiments have demonstrated the exceptional performance of our approach. It achieves a high level of detection accuracy on DeepFake images and showcases state-of-the-art generalization capabilities when confronted with previously unknown forgery methods. Moreover, it exhibits robustness even in the presence of image distortions.