CDM_data_anonymisation

ANONYMISATION METHODS

Make sample id's

given_name - remove
surname - remove
gender - leave it
birthdate - banding to year - Laura
country_of_birth - banding to continent - Laura
current_country - leave it
phone_number - remove - hash together
postcode - banding to partial postcode (first 2 characters) - Asude
national_insurance_number - remove - hash together
bank_account_number - remove -hash together

Rest of variables band for public but not for research?
weight - Asude - discuss Thomas
height - Asude - discuss Thomas
blood group - leave it - Laura
avg_n_drinks_per_week - banding (0-2,2-4) into low moderate heavy - Catherine
avg_n_cigret_per_week - banding (0-100, 100-200, etc) into low moderate heavy - Catherine
education_level - leave it
n_countries_visited - band to 0, 1, 2-5, 5+ (can also cahnge later to levels) can do levels of 5 - Ogi
cc_status - keep it for now, can check later

smoking, drinking, cc status, numbers of countries visited,

Name		Name	Last commit message	Last commit date
Latest commit History 81 Commits
.DS_Store		.DS_Store
Asude Banding Updated.ipynb		Asude Banding Updated.ipynb
Asude_FINAL_final.ipynb		Asude_FINAL_final.ipynb
CDM_Coursework_2_questions.pdf		CDM_Coursework_2_questions.pdf
CDM_Group4_Coursework.ipynb		CDM_Group4_Coursework.ipynb
Catherine.ipynb		Catherine.ipynb
Encrypting Direct Identifiers with Key for Team Panda.ipynb		Encrypting Direct Identifiers with Key for Team Panda.ipynb
FINAL_final.ipynb		FINAL_final.ipynb
Laura.ipynb		Laura.ipynb
PostcodeRegions.xlsx		PostcodeRegions.xlsx
README.md		README.md
actual_final copy.ipynb		actual_final copy.ipynb
actual_final.ipynb		actual_final.ipynb
customer_information.csv		customer_information.csv
final.ipynb		final.ipynb
final_data_gov.csv		final_data_gov.csv
final_data_res.csv		final_data_res.csv
final_ls_gv.csv		final_ls_gv.csv
final_ls_res.csv		final_ls_res.csv
gv_k_3.csv		gv_k_3.csv
ogi.ipynb		ogi.ipynb
output.png		output.png