Bài giảng Bảo mật cơ sở dữ liệu: Security methods for statistical databases - Trần Thị Kim Chi

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Bài giảng "Bảo mật cơ sở dữ liệu: Security methods for statistical databases" trình bày các nội dung: Laws, HIPAA compliance, types of statistical databases, security methods, query set restriction, microaggregation, output perturbation,... Mời các bạn cùng tham khảo.

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Nội dung Text: Bài giảng Bảo mật cơ sở dữ liệu: Security methods for statistical databases - Trần Thị Kim Chi

Security Methods for Statistical Databases
Introduction § Statistical Databases containing medical information are often used for research § Some of the data is protected by laws to help protect the privacy of the patient § Proper security precautions must be implemented to comply with laws and respect the sensitivity of the data
Accuracy vs. Confidentiality Accuracy – Confidentiality – Researchers Patients, laws want to extract and database accurate and administrators meaningful data want to maintain the privacy of patients and the confidentiality of their information
Laws § Health Insurance Portability and Accountability Act – HIPAA (Privacy Rule) § Covered organizations must comply by April 14, 2003 § Designed to improve efficiency of healthcare system by using electronic exchange of data and maintaining security § Covered entities (health plans, healthcare clearinghouses, healthcare providers) may not use or disclose protected information except as permitted or required § Privacy Rule establishes a “minimum necessary standard” for the purpose of making covered entities evaluate their current regulations and security precautions
HIPAA Compliance § Companies offer 3rd Party Certification of covered entities § Such companies will check your company and associating companies for compliance with HIPAA § Can help with rapid implementation and compliance to HIPAA regulations
Types of Statistical Databases § Static – a static § Dynamic – changes database is made continuously to reflect once and never real-time data changes § Example: most online § Example: U.S. Census research databases
Security Methods § Access Restriction § Query Set Restriction § Microaggregation § Data Perturbation § Output Perturbation § Auditing § Random Sampling
Access Restriction § Databases normally have different access levels for different types of users § User ID and passwords are the most common methods for restricting access § In a medical database: § Doctors/Healthcare Representative – full access to information § Researchers – only access to partial information (e.g. aggregate information)
Query Set Restriction § A query-set size control can limit the number of records that must be in the result set § Allows the query results to be displayed only if the size of the query set satisfies the condition § Setting a minimum query-set size can help protect against the disclosure of individual data
Query Set Restriction § Let K represents the minimum number or records to be present for the query set § Let R represents the size of the query set § The query set can only be displayed if K R
Query Set Restriction Query 2 Query 1 Original Database Query 2 Query Results K Results Query 1 K Query Results Results
Microaggregation § Raw (individual) data is grouped into small aggregates before publication § The average value of the group replaces each value of the individual § Data with the most similarities are grouped together to maintain data accuracy § Helps to prevent disclosure of individual data
Microaggregation § National Agricultural Statistics Service (NASS) publishes data about farms § To protect against data disclosure, data is only released at the county level § Farms in each county are averaged together to maintain as much purity, yet still protect against disclosure
Microaggregation Age Microaggregated Age 10 11.67 12 Average 11.67 13 11.67 57 56.67 54 Average 56.67 59 56.67
Microaggregation User Original Averaged Microaggregated Data Data
Data Perturbation § Perturbed data is raw data with noise added § Pro: With perturbed databases, if unauthorized data is accessed, the true value is not disclosed § Con: Data perturbation runs the risk of presenting biased data
Data Perturbation User 1 Noise Added Original Perturbed Database Database User 2
Output Perturbation § Instead of the raw data being transformed as in Data Perturbation, only the output or query results are perturbed § The bias problem is less severe than with data perturbation
Output Perturbation Query User 1 Results Noise Added to Results Original Database Query Results User 2
Auditing § Auditing is the process of keeping track of all queries made by each user § Usually done with up-to-date logs § Each time a user issues a query, the log is checked to see if the user is querying the database maliciously