3 results about "Consumer privacy" patented technology

The application relates to the technical field of homomorphic encryption, and discloses a homomorphic encryption protection system for flower consumer privacy image features, which comprises the following steps: acquiring the historical actual quality of interactive flowers, the apoptosis metabolic rate constant and the absolute elapsed time, constructing a multi-dimensional initial continuous data tensor which is objectively attenuated with time, mapping the tensor into equivalent algebraic potential energy within the limit of the truncation order and performing dimension orthogonal elimination, combining Gaussian bottom noise to be packaged into polynomial ciphertext, using fitness group intelligence optimization to obtain optimal evolution damping, fusing the elapsed time to output stable state ciphertext under the cipher state, then performing orthogonal inner product with external seasonal market ciphertext, and calling a public key to perform algebraic dimension reduction convergence, and finally using an isolated private key to crack and reverse restore into a preference concentration on the terminal side. The application effectively suppresses the cipher state noise divergence caused by multi-dimensional asynchronous metabolism, and realizes the safe evolution and accurate evaluation of the privacy elements according to the natural law.

Provided is a distributed application that shifts certain server-side operations from geolocation analytics platforms to client computing devices to enhance consumer privacy and the collection and use of potentially sensitive, personal data about an individual and their mobile device.

The invention relates to a coscheduling method and a coscheduling system for privacy protection of productive disappears based on group learning and multi-agent reinforcement learning. The coscheduling method comprises the following steps of: constructing a coscheduling architecture between a productive disappears model and productive disappears; based on the architecture, carrying out partial observable Markov game modeling of multi-producer collaborative scheduling under incomplete information, and constructing a group learning-based decentralized privacy protection collaborative scheduling framework; and an optimal transaction strategy is solved in a decentralized privacy protection collaborative scheduling framework through a PS-MAPPO (Privacy Protection State Enhanced Multi-agent Near-end Policy Optimization Algorithm), so as to realize multi-yield disagger privacy protection collaborative scheduling. The method can provide core support for constructing a next-generation virtual power plant and a distributed energy aggregation system which are safe, credible, efficient, cooperative and elastically extensible, and has wide application prospects in the fields of novel power system construction, park-level comprehensive energy management, multi-micro-grid interconnection operation and the like.