37 results about "Privacy model" patented technology

Embodiments of the present invention address deficiencies of the art in respect to privacy compliance assessment for computer software and provide a method, system and computer program product for a privacy model framework for software applications. In one embodiment, a privacy modeling data processing system can be provided. The privacy modeling data processing system can include a modeling framework configured for communicative coupling to a software application. The modeling framework can capture information flows from requests to and responses from a coupled software application, and can rules-based process the captured information flows for privacy rules to generate a privacy compliance report for the software application.

The invention discloses a graded privacy protection method in hierarchical data release. The graded privacy protection method comprises the following steps: firstly, grading sensitive attributes of original hierarchical data according to actual sensitivity; then, limiting the appearing frequency of the sensitive attributes with different grades in an equivalence class according to a divided gradesetting frequency parameter alphai; then realizing a given privacy model by adopting a clustering method. By adopting the graded privacy protection method disclosed by the invention, the problem of privacy leakage, caused by close-in attack, of the sensitive attributes of an existing hierarchical data release technology is solved very well, the protection intensity on the privacy protection of thehierarchical data is improved and the safety of the data is enhanced; the graded privacy protection method in the hierarchical data release, disclosed by the invention, has certain real meaning and practical value in the protection of the hierarchical data.

The invention provides a spatial aggregation based trajectory data privacy protection method and system. According to the method, an individual privacy risk metric value is acquired through establishment of a privacy model, the individual privacy risk metric value and the actual condition of urban population risk spatial distribution are compared, privacy risk protection is established, and an effect after privacy protection is evaluated. The method is simple, low in calculation complexity and applicable to spatial data with large data volume.

The invention provides a system solution for protecting position privacy on the premise of strictly ensuring privacy, belonging to the field of data information security. For most spatial transformation techniques, the proposed method relies on semantic privacy model, such as k-anonymous, or ad hoc uncertainty models that do not provide strict privacy. Many of them consider only static scenarios or disturbance locations on a single timestamp, rather than the temporal dependency of the mobile user's location, and are therefore vulnerable to various inferential attacks to propose solutions. Firstly, according to the topological relation of the geographic space, the algorithm is proposed to calculate the privacy level of each region on the map. Secondly, a differential privacy based onsigma-location set is proposed to protect the real position of each time point, and the sensitivity package is used to capture the geometric meaning of two-dimensional sensitivity. Finally, a special position perturbation mechanism is proposed to implement the algorithm based on sigma-Differential privacy of the location set.

The invention discloses a location anonymous method for continuous queries. The method comprises the steps that when an anonymous query is received, each unexpired query to be anonymous in a query set to be anonymous is detected; when the unexpired queries to be anonymous meet a quality model, the queries to be anonymous are inserted into a candidate anonymous set; when the detection of the queries to be anonymous in the query set to be anonymous is finished, and the number of queries in the candidate anonymous set is larger than or equal to that of a preset anonymity degree, the candidate anonymous set forms an anonymity circle, and whether the anonymity circle meets a privacy model or not is judged; when the anonymity circle meets the privacy model, the center of the anonymity circle is adjusted, and the anonymity circle is returned. The invention further discloses a location anonymous device for the continuous queries and a privacy protection system. The privacy protection on the continuous queries is improved, and location service quality and anonymity efficiency are improved.

The invention provides a privacy model training method based on a small amount of public data and an electronic device. The privacy model training method comprises steps of obtaining N neural networkteacher models by using a plurality of trainings; respectively inputting a small amount of public data xi into the N neural network teacher models to obtain a statistical voting result of each publicdata xi for each label k; noise being added to all statistical voting results, and public data xi and corresponding labels meeting the differential privacy principle being obtained; optimizing an adversarial generative network through a large number of random noise vectors and a pre-trained discriminant neural network, and generating a large number of label-free data; and jointly training a student model through the public data xi meeting the differential privacy principle, the corresponding label and a large amount of unlabeled data pair pre-trained auto-encoders to obtain a private student model. According to the method, only a small amount of public data is needed to train one private student model, physical isolation and network isolation of sensitive data are realized, and a problem of low accuracy of the private student model is solved.

The invention belongs to the technical field of information security, and relates to a sensitive label track data publishing method based on a graph differential privacy model. The method comprises the steps: clustering and generalizing tracks, performing graph-based time-space point differential privacy and publishing track data with privacy labels. In the first stage, firstly, all space-time points are divided into hot spot areas and abnormal values, and then the center positions of the hot spot areas are used for generalizing each specific position in the trajectory data; in the second stage, a noise map is established, hot spots, abnormal values and privacy tags are mapped to a directed weighted graph, different privacy budgets are set for different vertexes in order to control the size of the added noise and protect the availability of the data as much as possible, and then Laplace noise is added to achieve differential privacy; in a publishing stage, each record in the generalized trajectory data set is restored, the overhead point is selected, the trajectory data is generated according to the heuristic expression, anonymous data is obtained, and high availability of the datais ensured.

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.

The invention discloses a high-dimensional position trajectory data privacy protection publishing system and method based on TP-MFSA suppression publishing, and belongs to the field of privacy protection algorithms. In order to resist an identity link attack and an attribute link attack caused by a track sequence in a high-dimensional track data release process and consider the data set mining practicability after anonymous processing, the invention provides a high-dimensional position trajectory data privacy protection publishing system and method based on TP-MFSA inhibition publishing. The method comprises: finding a minimum violation sequence set with privacy leakage according to a KCL-privacy model; judging the suppression mode of each time-space point in the MVS, namely, local suppression or global suppression; and designing an inhibition sequence model of each time-space point in the MVS based on the privacy protection intensity and the data practicability, and performing inhibition operation on the data set according to the sequence to obtain a publishable high-dimensional trajectory data set.

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 relates to a privacy model training method and device, computer equipment and a storage medium. The method of one embodiment comprises the steps of issuing a model training task based on a to-be-trained model; receiving a training joining request sent by each user terminal, generating a user set according to the request time of the training joining request of each user terminal, and sending the user set to each user terminal; dividing the users in the user set into user groups; in any iteration training process, receiving a model training ciphertext which is sent by each user terminal in the user set and carries a user terminal ciphertext, a model parameter ciphertext and training iteration times; based on the user group to which each user terminal belongs, performing key aggregation processing on the user terminal ciphertext of each user terminal to obtain a key aggregation processing result; and when the key aggregation processing result passes verification, performing model aggregation processing on the model parameter ciphertext of each user terminal to obtain a model aggregation result. By adopting the method, the safety and the stability during model training can be improved.

The invention belongs to the technical field of information security, and provides a privacy measurement algorithm for structured data in a data release environment. Firstly, a data source collects user information and generates a user information abstract, an external data set is screened according to abstract information, and attribute similarity is calculated to distinguish private attributes and non-private attributes. Then, external and internal average information amount and distribution information amount of the private attributes are calculated, sensitive attributes for the private attributes are priced and identified based on demand and time loss, quasi-identifiers are extracted by applying a maximum entropy principle, and risk elements are identified in the data. Finally, the data privacy is measured according to the privacy model, a privacy-oriented anonymous technology is given, and it can be proved that the privacy and availability under the orientation reach the best balance.

A database system (1) and a method (100), comprising a database system memory (3) and at least a first database server (9). The database system memory (3) stores a database of data records (7) and shared program instructions (51) between first and second database users (21, 31). The shared program instructions (51) define a privacy model (13) comprising privacy restrictions (14, 23, 33) for the first and second database users (21, 31), respectively, and specify an authorization model (19) comprising a first set of authorizations (25', 35') that permit the first database user (21) to manipulate a first subset (27) of the data records consistent with the first user's privacy restrictions (23', 33') and a second set of authorizations (25'', 35'') that permit the second user (31) to manipulate a second subset (37) of the data records consistent with the second user's privacy restrictions (23'', 33''). The database server (9) includes a processor (11) configured to execute the shared program instructions (51), wherein the shared program instructions (51), when executed by the processor (11): (1) process (110) a transaction (53) submitted by the first or second database user (21, 31); (2) determine (120) whether the transaction (53) conforms to the privacy and authorization models (13, 19); and (3) if the transaction passes step 2, commit (130) the transaction (53) and manipulate (55) the first or second subset of data records (27, 37) consistent with privacy and authorization models (13, 19).

The invention discloses a trusted traceability method and system based on blockchain, and a storage medium, and belongs to the field of open use of security data and security results. The method comprises the steps: establishing an initial database, wherein the initial database is open, and the security capability type and the security capability of the initial database can be dynamically expanded; constructing a security information chain in a local area network, carrying out data uplink, process uplink and capability uplink on source data stored in the initial database, constructing a privacy model, and carrying out privacy calculation on the uplink data; credibly opening the security data after privacy calculation, expanding the security data, and upgrading the security data into a security capability resource pool. The method further enriches the security data, process and capability of the uplink, constructs an open tenant defense alliance in the extensive local area network, realizes sharing of security resource pool customer security protection results in the local area network, and is suitable for the field of multi-target customer group security of governments, enterprises and the like.