Blood glucose/blood pressure fuzzy monitoring method with privacy protection

Abstract

The invention discloses a blood glucose / blood pressure fuzzy monitoring method with privacy protection. The blood glucose / blood pressure fuzzy monitoring method comprises the following steps: 1, collecting blood glucose and blood pressure data by utilizing health monitoring equipment, and carrying out preprocessing; 2, performing fuzzy processing on the blood glucose data based on the knowledge ofthe health field, and defining fuzzy division conforming to the knowledge of the blood glucose and blood pressure health field, so the fuzzy division can reliably convert the original sensitive bloodglucose / blood pressure data into a reliable and easily understood fuzzy message; 3, performing fuzzification processing on the blood pressure data based on the knowledge of the health field; 4, defining and storing fuzzy language variables, and performing blood glucose / blood pressure state fuzzy real-time monitoring and historical query. The method is good in privacy protection effect, reliable in result, easy to understand and easy to deploy and apply.

Description

technical field [0001] The invention relates to the field of intelligent medical care, and relates to a blood sugar / blood pressure fuzzy monitoring method with privacy protection. Background technique [0002] The popularization and application of portable health monitoring devices such as wearable devices has continuously increased the privacy and security requirements of users for their health information, especially worrying about the leakage of some sensitive health data. Existing full strategies such as k-anonymity technology can prevent data attackers from judging which individual’s private information the data belongs to, but while improving the privacy protection effect, the data will be distorted and affect usability; On the one hand, the sensitive data of the k-anonymity model will be attacked by background knowledge due to the lack of constraints, and the attacker can use this to determine the connection between sensitive data and people, so that private data will...

AI Technical Summary

Problems solved by technology

Existing full strategies such as k-anonymity technology can prevent data attackers from judging which individual’s private information the data belongs to, but while improving the privacy protection effect, the data will also be distorted and affect usability; On the one hand, the sensitive data of the k-anonymous model will be attacked by background knowledge due to the lack of constraints. Attackers can use this to determine the connection between sensitive data and people, so private data will also be leaked

If sensitive health data is encrypted and stored to a third party, the calculation of encryption will be expensive and affect the availability of data, and the third party is not absolutely trustworthy

In addition, wearable devices used to collect health data generally have limited performance and are not suitable for overly complex encryption and data availability recovery calculations. Therefore, it is necessary to design a lightweight privacy protection scheme

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