2 results about "K-anonymity" patented technology

The invention relates to the technical field of data security, in particular to a scientific and technological achievement transformation platform-oriented privacy data desensitization method, which comprises the following steps of: acquiring scientific and technological achievement data from a scientific and technological achievement transformation platform, and performing word segmentation and keyword extraction on text contents in the scientific and technological achievement data; performing semantic clustering on the keywords based on the feature vectors of the keywords to construct a generalization hierarchical tree; carrying out weighted fusion on the sensitivity and the rare degree of the keyword to obtain data privacy; and performing equal-length interval division according to the index size of the data privacy, and allocating a self-adaptive K value for generalization by adopting a K anonymity algorithm for each divided group so as to perform generalization desensitization processing on keywords in the scientific and technological achievement data. According to the method, the validity of data loss caused by excessive generalization of the data can be avoided on the premise of ensuring the privacy of high-risk sensitive data in the scientific and technological achievement data.

The application relates to the field of artificial intelligence and big data processing technology, and discloses a user portrait construction method based on AI big data, which comprises the following steps: performing differential privacy injection preprocessing on original user behavior logs; constructing a dynamic generalization unit satisfying k anonymity and having minimum generalization error; mapping the unit to a low-dimensional embedding space to generate an anonymous sequence; performing high-fidelity sequence reconstruction by using a conditional generative adversarial network; separating long-term interest and short-term intention by multi-granularity time sequence decomposition, respectively aggregating features by using a double-channel attention mechanism, and finally fusing to generate a high-fidelity user portrait vector. The system comprises corresponding function modules. The application significantly improves the behavior fidelity of the user portrait and the recommendation recall rate under the premise of meeting the differential privacy and k anonymity legal standards.