Abstract: Disclosed herein are system, method, and computer program product embodiments for generating and chaining zero-knowledge proofs (“ZKPs”) using proof chaining. A proof may be divided into different components to analyze data in a series and/or parallel manner using chained proof systems. The proof systems may generate hash values indicating that particular functions were applied to the instances of the underlying data. The hash values may prove that a particular function was applied to instances of the underlying data. Using the proof chaining configuration, a data supply system may generate a ZKP allowing a data requesting system to trust a function result without needing to receive the underlying data.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating and chaining zero-knowledge proofs (“ZKPs”) using proof chaining. A proof may be divided into different components to analyze data in a series and/or parallel manner using chained proof systems. The proof systems may generate hash values indicating that particular functions were applied to the instances of the underlying data. The hash values may prove that a particular function was applied to instances of the underlying data. Using the proof chaining configuration, a data supply system may generate a ZKP allowing a data requesting system to trust a function result without needing to receive the underlying data.

Abstract: A method and a system for generating private randomness and/or public randomness are provided. A measurement of a public randomness and/or a measurement of a plaintext may be obtained, and a desired size of a private randomness may be determined based on the measurements. The measurements may be based on one or more Tsallis entropy values of the public randomness and/or the plaintext. Private randomness may be generated, the private randomness may be encrypted, the private randomness may be deleted so that the private randomness is unrecoverable, and the encrypted private randomness may be published. Encrypted private randomness and public randomness may be obtained, and a new public randomness may be generated based on the public randomness and the encrypted private randomness.

Abstract: A method and a system for generating public randomness are provided. A measurement of a public randomness and/or a measurement of a plaintext may be obtained, and a desired size of a private randomness may be determined based on the measurements. Private randomness may be generated, the private randomness may be encrypted, the private randomness may be deleted so that the private randomness is unrecoverable, and the encrypted private randomness may be published. Encrypted private randomness and public randomness may be obtained, and a new public randomness may be generated based on the public randomness and the encrypted private randomness.

Abstract: A method and a system for generating public randomness are provided. A measurement of a public randomness and/or a measurement of a plaintext may be obtained, and a desired size of a private randomness may be determined based on the measurements. Private randomness may be generated, the private randomness may be encrypted, the private randomness may be deleted so that the private randomness is unrecoverable, and the encrypted private randomness may be published. Encrypted private randomness and public randomness may be obtained, and a new public randomness may be generated based on the public randomness and the encrypted private randomness.

Abstract: A method and a system for generating private randomness and/or public randomness are provided. A measurement of a public randomness and/or a measurement of a plaintext may be obtained, and a desired size of a private randomness may be determined based on the measurements. The measurements may be based on one or more Tsallis entropy values of the public randomness and/or the plaintext. Private randomness may be generated, the private randomness may be encrypted, the private randomness may be deleted so that the private randomness is unrecoverable, and the encrypted private randomness may be published. Encrypted private randomness and public randomness may be obtained, and a new public randomness may be generated based on the public randomness and the encrypted private randomness.

Abstract: A social incentive system is described herein that formalizes information propagation through social networks in a structured and systematic way. The system provides incentives and rewards to entities who participate in propagating the information, allowing heavy influencers to gain from their influence while the marketer rewards them. The system provides one or more tools for creation and design of social incentive plans with rewards for socially distributing information, including marketing campaigns. In particular, the system introduces a semantic framework where marketers can create structured incentive plans for rewarding consumers and distribution platforms for distributing information through social networks. As users complete specified activities they earn points, and the points can be redeemed for various incentives, such as cash, debit cards, prizes, merchandise, subscriptions, and so forth. The framework is robustly designed to avoid abuse and allow for fraud detection.