If you ever have to give your age, a price you bought something for, how much you make an hour or a year, etc. you may want to give people a range or something less than precise. Many people might pick a number range where the actual number is almost in the middle of the range. That is not very good for security so you can enhance it a bit.
If your age is 45 and you would like to fuzz the number a bit you can build two ranges and put them together afterwards.
You choose a half range number that gets fuzzed first. We will use the number 10. The first random generation of 1-10 range as we chose ten as our first half range number to fuzz. We will choose 7. Our below range is now 45-7=38. Now we fuzz the top end. Our second range number will be 4. We now have 45+4=49. Our complete range then is a compilation of the two range numbers we have fuzzed.
Our range then is 38 to 49. That is a range of 11. If you wish to give the answer in a range form you can give them the range of 38 to 49 now. If the other person assumes the age is in the middle their assumed value with be half of 11 plus 38 with a value of 43.5 and a round up of 44 or round down of 43.
To give an exact value, we now randomize a number that we will subtract or add from one of the ends. You can choose randomly which end you will work with each time. This time we will add to the lower end. Our random number between 11 is 3. We now have 38+3=41.
The three optional assumptions or direct answers for the example are 41, 43, and 44 for the actual value being fuzzed of 45.
This system could be enhanced by adding extra rules that give you a way to determine if you want to add or subtract based on the randomized value that is chosen to fuzz each of the range extremes.
EDIT:
Turns out there is a thing called data anonymization already used for data sets.
Data anonymization has been defined as a “process by which personal data is irreversibly altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party.” [1] Data anonymization may enable the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization.
In the context of medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information. The name, address, and full postcode must be removed, together with any other information which, in conjunction with other data held by or disclosed to the recipient, could identify the patient.[2]
There will always be a risk that anonymized data may not stay anonymous over time. Pairing the anonymized dataset with other data, clever techniques and raw power are some of the ways previously anonymous data sets have become de-anonymized; The data subjects are no longer anonymous.
De-anonymization is the reverse process in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source.[3] Generalization and perturbation are the two popular anonymization approaches for relational data.[4] The process of obscuring data with the ability to re-identify it later is also called pseudonymization and is one-way companies can store data in a way that is HIPAA compliant.[5]
There are five types of data anonymization operations: generalization, suppression, anatomization, permutation, and perturbation.[6]