Skip to main content

Computed IDs and privacy implications

Thirty years ago, a lot of US states thought it would be a good idea to compute someone’s drivers license number (DLN) from their personal information [1]. In 1991, fifteen states simply used your Social Security Number as your DLN. Eleven other states computed DLNs by applying a hash function to personal information such as name, birth date, and sex. A few other states based DLNs in part but not entirely on personal information.

Presumably things have changed a lot since then. If you know of any states that still do this, please let me know in the comments. Even if states have stopped computing DLNs from personal data, I’m sure many organizations still compute IDs this way.

The article I stumbled on from 1991 gave no hint perhaps encoding personal information into an ID number could be a problem. And at the time it wasn’t as much of a problem as it would be now.

Why is it a problem if IDs are computed from personal data? People don’t realize what information they’re giving away. Maybe they would be willing to give someone their personal information, but not their DLN, or vice versa, not realizing that the two are equivalent. They also don’t realize what information about them someone may already have; a little bit more info may be all an attacker needs. And they don’t realize the potential consequences of their loss of privacy.

In some cases the hashing functions were complicated, but not too complicated to carry out by hand. And even if states were applying a cryptographic hash function, which they certainly were not, this would still be a problem for reasons explained here. If you have a database of personal information, say from voter registration records, you could compute the hash value of everyone in the state, or at least a large enough portion that you stand a good chance of being able to reverse a hashed value.

Related posts

[1] Joseph A. Gallian. Assigning Driver’s License Numbers. Mathematics Magazine, Vol. 64, No. 1 (Feb., 1991), pp. 13-22.



from John D. Cook https://ift.tt/2PHumIW
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...
Post a Comment
Read more

The con behind every wedding

With her marriage on the rocks, one writer struggles to reconcile her cynicism about happily-ever-after as her own children rush to tie the knot A lavish wedding, a couple in love; romance was in the air, as it should be when two people are getting married. But on the top table, the mothers of the happy pair were bonding over their imminent plans for … divorce. That story was told to me by the mother of the bride. The wedding in question was two summers ago: she is now divorced, and the bridegroom’s parents are separated. “We couldn’t but be aware of the crushing irony of the situation,” said my friend. “There we were, celebrating our children’s marriage, while plotting our own escapes from relationships that had long ago gone sour, and had probably been held together by our children. Now they were off to start their lives together, we could be off, too – on our own, or in search of new partners.” Continue reading... The Guardian http://ift.tt/2xZTguV October 07, 2017 at 09:00AM
Post a Comment
Read more