https://github.com/lukasjhan/data-de-identification-using-symmetric-data

https://github.com/lukasjhan/data-de-identification-using-symmetric-data

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• Host: GitHub
• URL: https://github.com/lukasjhan/data-de-identification-using-symmetric-data
• Owner: lukasjhan
• Created: 2023-04-30T08:09:37.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:39:12.000Z (over 1 year ago)
• Last Synced: 2024-10-24T08:52:35.855Z (3 months ago)
• Language: Python
• Size: 157 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Data De-Identification using Symmetric Data (BoB 8)

## ABSTRACT

As data usage increases, the risk of personal information leakage is increasing. To reduce such risk, personal information must be used de-identified.

### ISO/IEC 20889

De-identification is the process used to prevent someone's personal identity from being revealed. For example, data produced during human subject research might be de-identified to preserve the privacy of research participants.

### Technique

#### Pseudonymization

- Pseudonymization

  - Heuristic pseudonymization

  - Encryption 

  - Swapping

- Aggregation

  - Aggregation

  - Micro Aggregation

  - Rounding

  - Rearrangement

- Data Reduction

  - Removing Identifier

  - Partical Removing Identifier

  - Reducing Records

- Data Suppression

  - Random Rounding

  - Data Range

  - Controlled Rounding

- Data Masking

  - Adding Random Noise

  - blank and impute

#### k-anonymization

- k-anonymization

- l-diversity

- t-closeness

- Differential Privacy

## Result

- data de-identification guide reports of each industry(medical, manufacturing, distribution etc.) 

- library for creating symmetric data

- library for data de-identification and re-identification in big data environments like hadoop and spark