Data mixing
Data mixing (English: randomization) is at a set of techniques that change the veracity of data to remove the close link between the data and the person. It is based on reversing the order of the data values in the columns of a table so that the real relationship between certain indicators is no longer traceable. The overall picture of the data remains unchanged.
Example
In the example below, the data in the "City" column will be mixed. It should be remembered that with data blending it will no longer be possible to determine the relationship between parameters, calculate correlations and regressions, so it is recommended to use this method for data that are only generally representative of the study population.
Original data
| ID | Age | City | Diagnosis |
|---|---|---|---|
| 101 | 35 | Sigulda | Hypertension |
| 102 | 28 | Ape | Diabetes |
| 103 | 40 | Dobele | Migraine |
| 104 | 32 | Suntaži | Multiple sclerosis |
| 105 | 22 | Riga | Asthma |
| 106 | 44 | Liepaja | Hypertension |
Anonymised dataset after urban blending
| ID | Age | City | Diagnosis |
|---|---|---|---|
| 101 | 35 | Suntaži | Hypertension |
| 102 | 28 | Sigulda | Diabetes |
| 103 | 40 | Dobele | Migraine |
| 104 | 32 | Ape | Multiple sclerosis |
| 105 | 22 | Liepaja | Asthma |
| 106 | 44 | Riga | Hypertension |
Data mixing
Data mixing (English: randomization) is at a set of techniques that change the veracity of data to remove the close link between the data and the person. It is based on reversing the order of the data values in the columns of a table so that the real relationship between certain indicators is no longer traceable. The overall picture of the data remains unchanged.
Example
In the example below, the data in the "City" column will be mixed. It should be remembered that with data blending it will no longer be possible to determine the relationship between parameters, calculate correlations and regressions, so it is recommended to use this method for data that are only generally representative of the study population.
Original data
| ID | Age | City | Diagnosis |
|---|---|---|---|
| 101 | 35 | Sigulda | Hypertension |
| 102 | 28 | Ape | Diabetes |
| 103 | 40 | Dobele | Migraine |
| 104 | 32 | Suntaži | Multiple sclerosis |
| 105 | 22 | Riga | Asthma |
| 106 | 44 | Liepaja | Hypertension |
Anonymised dataset after urban blending
| ID | Age | City | Diagnosis |
|---|---|---|---|
| 101 | 35 | Suntaži | Hypertension |
| 102 | 28 | Sigulda | Diabetes |
| 103 | 40 | Dobele | Migraine |
| 104 | 32 | Ape | Multiple sclerosis |
| 105 | 22 | Liepaja | Asthma |
| 106 | 44 | Riga | Hypertension |