37 results about "Privacy model" patented technology

Systems and / or methods that mitigate overexposure of shared content and that facilitate improving security of content in a computing environment are provided. In particular, the systems and methods involves monitoring resource activities such as user interactions (between users and / or between users and resources), email activity, share space activity, shared content activity, distribution lists, contact lists, personnel or organization structures, and the like. Data can be collected as one or more resource activities are monitored and then analyzed to determine where, if any, changes have taken place. For example, if a user has been removed from a distribution list or a user has not accessed the shared content within a time period, a recommendation can be formulated to suggest an adjustment to the affected access rights. Furthermore, the systems and / or methods can be integrated with other applications such as an email client, whereby an out-of-office indicator can leave access rights as they are.

The invention provides a privacy protection method and system based on a high-risk frequent active point replacement policy. The privacy protection method comprises the following steps: establishing a privacy model, grading individual risks into zero-risk individuals, low-risk individuals and high-risk individuals based on the privacy model, hiding the high-risk individuals in the low-risk individuals for privacy protection, and evaluating a privacy protection result. The method is simple, is low in calculation complexity and can be applicable to spatial data with large data sizes.

The invention discloses a vehicle position privacy protection method based on group signature and reputation, which comprises a reputation model for encouraging more vehicles to cooperate with each other to form a group, a privacy model for evaluating the position privacy strength of the vehicle, a group forming and dynamic mixed region establishing mechanism, and a pseudonym replacement mechanism. Any vehicle in the vehicle-mounted ad hoc network actively requests establishment of a group from a control server according to the reputation value of the vehicle when the pseudonym of the vehicleis soon to be expired; the control server allocates a group leader role to the requesting vehicle and dynamically forms a mixed region according to the current position of the requesting vehicle; theother vehicles in the mixed region judge whether the cooperation is participated in or not to change the pseudonym according to the reputation value, privacy strength and the remaining time of the pseudonym of the other vehicles; and the reputation value of the vehicles can be dynamically adjusted. According to the method, more vehicles can be encouraged to join the group, thereby changing the pseudonym in cooperation and ensuring the position privacy of the vehicles; meanwhile, the vehicles do not need to keep a silent state in the formed mixed region but broadcast messages as a group identity so that the running safety of the vehicles is guaranteed; furthermore, resource expenditure caused by unnecessary frequent change of the pseudonym can be effectively reduced.

The invention discloses a vehicle position privacy protection method of a vehicular ad hoc network. The method comprises the following steps: a privacy model is first set to assess a current position privacy of different vehicles so as to reflect a reputation module and a mix-zone criterion of a cooperation degree of the current vehicle; any one vehicle in the vehicular ad hoc network determines whether to cooperate and change a pseudo name to protect a vehicle i according to a current reputation value of the vehicle i sending a pseudo name request and based on a judgment whether the one vehicle is within a mix-zone of the vehicle i; and the reputation value of the one vehicle can be dynamically adjusted according to the current position privacy of the one vehicle and pseudo name residual time. The method of the invention can meet the requirement of protecting the current position of the vehicle and can substantially reduce the energy cost; and the method has the advantages that more vehicles are involved in the pseudo name change and the safety of the position privacy of the vehicular wireless network is greater.

The invention discloses a hierarchical privacy protection method in the release of hierarchical data. First, the sensitive attributes of the original hierarchical data are classified according to the actual sensitivity, and then the frequency parameter α is set according to the divided grades. i To limit the frequency of different levels of sensitive attributes in the equivalence class, and then use the clustering method to achieve a given privacy model. The present invention well solves the problem of privacy leakage caused by the proximity attack on the sensitive attributes of the existing hierarchical data publishing technology, improves the protection of hierarchical data privacy, and enhances the security of data; the present invention is for The protection of hierarchical data has certain practical significance and practical value.

The invention belongs to the field of mobile edge technology, and discloses a mobile streaming media edge cooperative distribution device and method based on differential privacy, including a data privacy module and a cooperative decision-making module; when the streaming media collaborative transmission cycle starts, the resource status of the edge server is collected Information, request video clip information of streaming media viewing users; Obtain blurred request information of streaming media viewing users; Establish geographic location privacy model; Obtain cooperation information of relay transmission users; Establish collaboration cost privacy model, reward function decision model ; Build a collaborative user matching model, and determine the streaming media content distribution path according to the reward function decision model; complete the edge collaborative distribution of streaming media content, and update relevant information. The invention solves the problem that the streaming media collaborative transmission cannot guarantee the privacy of sensitive data in the prior art, can prevent user privacy data from leaking, and reduces the cost of streaming media collaborative distribution.

The invention discloses a multi-attribute data privacy removing method taking practicability into consideration. The method comprises the following steps of S1, importing preprocessed multi-attribute data; S2, defining necessary attributes and sensitive attributes according to attribute descriptions, setting pre-grouping rules of the necessary attributes, and defining the sequences of the necessary attributes according to the attribute characteristics; S3, establishing a privacy exposure risk tree, and taking the attribute sequences and the pre-grouping rules in the step S2 as hierarchical sequences of the privacy exposure risk tree and the basis for generating branches of each hierarchy; and S4, measuring result information according to coding risks of the sensitive attributes on nodes and borders of the privacy exposure risk tree. According to the method, the suitable method can be flexibly selected from a plurality of common grammar anonymization and difference privacy models for solving the privacy problem, thereby satisfying various privacy demands for different data. Through utilization of special advantages of the privacy exposure risk tree, a multi-dimensionally aggregated space is designed compactly.

A model training method based on federated learning is disclosed. In order to protect the privacy of the server (model parameters) from being leaked, the server uses a homomorphic encryption algorithm to encrypt the model parameter set and sends it to the node. Based on the principle of homomorphic encryption, the node uses the encrypted model parameters and local training samples to encrypt The model calculation in the state obtains the encrypted gradient. Then, based on the principle of homomorphic encryption, the node calculates the difference between the encrypted gradient and the encrypted random number, which is essentially a meaningless encrypted value. Then, the node uploads the encrypted value to the server. In addition, the server can use the SA protocol to obtain the sum of random numbers on each node without knowing the random number on each node. In this way, the server can restore the sum of the gradients generated by each node based on the encrypted value uploaded by each node and the sum of each random number, so that the model parameters can be updated.