With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf 1.

Researchers are studying whether digital transformation can increase corporate risk-taking. A study using data from Chinese companies between 2010 and 2019 shows that digital transformation does enhance risk-taking. This is achieved through two channels—reducing financial risk and enhancing enterprise value—and attracting media attention and improving information communication. Other factors like ownership, financing, industry competition, and policy uncertainty can also boost this effect. These findings offer significant contributions towards comprehending the influence of digital transformation on the real economy and offer practical insights to optimize the digital dividend and enhance the high-quality development of enterprises.

In this paper, we propose a new optimization for the Paillier’s additively homomorphic encryption scheme (Eurocrypt’99). At the heart of our optimization is a well-chosen subgroup of the underlying , which is used as the randomness space for masking messages during encryption. The size of the subgroup is significantly smaller than that of , leading to faster encryption and decryption algorithms of our optimization. We establish the one-wayness and semantic security of our optimized Paillier scheme upon those of an optimization (i.e., “Scheme 3”) made by Paillier in Eurocrypt’99. Thus, our optimized scheme is one-way under the partial discrete logarithm (PDL) assumption, and is semantically secure under the decisional PDL (DPDL) assumption. On the other hand, we present a detailed analysis on the concrete security of our optimized scheme under several known methods. To provide 112-bit security, our analysis suggests that a 2048-bit modulus N and a well-chosen subgroup of size 448-bit would suffice. We compare our optimization with existing optimized Paillier schemes, including the Jurik’s optimization proposed by Jurik in his Ph.D. thesis and the Paillier’s optimization in Eurocrypt’99. Our experiments show that, – the encryption of our optimization is about 2.7 times faster than that of the Jurik’s optimization and is about 7.5 times faster than that of the Paillier’s optimization;