Privacy by Design (PbD) is a significant concept here at Senzing. One of the predominant data categories ingested into G2 is Personally Identifiable Information (PII) and/or attributes that can be used to aid in uniquely identifying an entity. Keeping this data secure is crucial, sometimes in addition to organizational security measures.
In situations where you have data you do not wish to load into G2 as cleartext, or if you have data that has already been hashed in order to protect it, this data may be loaded in a pre-hashed state. Consider the following:
The table outlines 2 attributes first showing their clear text unhashed values followed by a pre-hashed unreadable value. In addition to being able to load the unhashed clear text values in G2 the pre-hashed values of these two attributes (and many others) can be loaded.
The current available attributes that can be mapped as pre-hashed and are understood by G2 are:
ACCOUNT_PREHASHED
ADDRESS_PREHASHED
CITIZENSHIP_PREHASHED
DOB_PREHASHED
DRLIC_PREHASHED
EMAIL_PREHASHED
GROUP_ASSOCIATION_PREHASHED
NATIONALITY_PREHASHED
NATIONAL_ID_PREHASHED
OTHER_ID_PREHASHED
PASSPORT_PREHASHED
PHONE_PREHASHED
PHYSICAL_PREHASHED
SOCIAL_PREHASHED
SSN_LAST4_PREHASHED
SSN_PREHASHED
TAX_ID_PREHASHED
WEBSITE_PREHASHED
The _PREHASHED suffix informs G2 these attributes will contain pre-hashed values upon ingestion. See Data Mapping for additional information on how to map your source data to G2 terms.
Example JSON and CSV mappings for the above attributes:
JSON
- Cleartext un-hashed
- {"SSN_NUMBER": "311-11-1111"}
- {"PHONE_TYPE": "CELL", "PHONE_NUMBER": "702-222-2222" }
- Pre-hashed
- {"SSN_NUMBER": "a1e89c6b142f25e26ea1de32f91a1958", "SSN_PREHASHED":"1" }
- { "PHONE_TYPE": "CELL", "PHONE_NUMBER": "1365094f8a2d60d772665c6809bebb3a", "PHONE_PREHASHED":"1" }
CSV
- Cleartext un-hashed
SSN_NUMBER ,SSN_PREHASHED,CELL_PHONE_NUMBER,CELL_PHONE_PREHASHED 311-11-1111, ,702-222-2222,
- Pre-hashed
SSN_NUMBER ,SSN_PREHASHED,CELL_PHONE_NUMBER ,CELL_PHONE_PREHASHED a1e89c6b142f25e26ea1de32f91a1958,1 ,1365094f8a2d60d772665c6809bebb3a,1
Note when using pre-hashed values the *_PREHASHED value is set to 1. The system will remember that this data is in a hashed form and process it correctly for entity resolution. Note: You can load attributes in both cleartext and pre-hashed form, G2 will keep them seperate and process them distinctly and appropriately.
Considerations for Fuzzy Matching
To accurately find matches between entities, one methodology used is fuzzy matching e.g. the phone numbers 345-743-6436, 345-643-6436 and 743-6436 are nearly identical, but have either a missing area code or typo in one of the digits.
Fuzzy matching is more challenging with hashed data. The hashes will be different even when the data is virtually identical. For this reason, when loading pre-hashed data it is best to also include attributes which provide partial data.
For example, in addition to PHONE_NUMBER you can map to PHONE_LAST_5 and PHONE_LAST_10; partial and incomplete versions of the phone number. Including these partial hashes allows matching against similar data even when hashed.
Consider the following phone numbers where only the hashes are loaded. The hashes of the whole phone number do not match, yet mapping the last column as PHONE_LAST_5 would still allow matching and this to be considered during entity resolution.
Comments
0 comments
Please sign in to leave a comment.