Advances in cryptology - CRYPTO -87 ; Conference on the theory and applications of cryptographic techniques : Proceedings
Zero-knowledge interactive proofsystems are a new technique which can be used as a cryptographic tool for designing provably secure protocols. Goldwasser, Micali, and Rackoff originally suggested this technique for controlling the knowledge released in an interactive proof of membership in a language, and for classification of languages. In this approach, knowledge is defined in terms of complexity to convey knowledge if it gives a computational advantage to the receiver, theory, and a message is said for example by giving him the result of an intractable computation. The formal model of interacting machines is described in. A proof-system (for a language L) is an interactive protocol by which one user, the prover, attempts to convince another user, the verifier, that a given input x is in L. We assume that the verifier is a probabilistic machine which is limited to expected polynomial-time computation, while the prover is an unlimited probabilistic machine.
كتب مشابهة
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI in banking : Practical applications and case studies
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Layce (Image data poisoning) = لايس (تسميم بيانات الصور )
The ongoing growth of image generative artificial intelligence models was paved with existing drawings and art pieces by great artists both past and present, and while generative models are very useful and helpful, there is the issue of the origin of the datasets trained on, and the morality of usage regarding copyrights and artistic identity. A novel line of defense that helps artists and visual content creators actively protect their pieces emerged, dubbed Data Poisoning and it works by misleading Artificial Intelligence models that attempt to use a Poisoned Image for training, or as a reference, as the Poisoned Image will appear to the human eye identical to the original art piece, while the Artificial Intelligence model will be seeing a remarkably different image, causing generative models to generate false positive results when given a prompt poisoned by the author or when trained on data poisoned by the original owner. This study aims to study image data poisoning methods and technologies, and build an application containing multiple image models, and poisoning models as well, accompanied by a Community for artists to share art and interact with each other.
