on anonymisation

An article in the New York Times covering a recent publication in Nature Communications on the ability to identify 99.98% of Americans from almost any dataset with fifteen covariates. And mentioning the French approach of INSEE, more precisely CASD (a branch of GENES, as ENSAE and CREST to which I am affiliated), where my friend Antoine worked for a few years, and whose approach is to vet researchers who want access to non-anonymised data, by creating local working environments on the CASD machines  so that data does not leave the site. The approach is to provide the researcher with a dedicated interface, which “enables access remotely to a secure infrastructure where confidential data is safe from harm”. It further delivers reproducibility certificates for publications, a point apparently missed by the New York Times which advances the lack of reproducibility as a drawback of the method. It also mentions the possibility of doing cryptographic data analysis, again missing the finer details with a lame objection.

“Our paper shows how the likelihood of a specific individual to have been correctly re-identified can be estimated with high accuracy even when the anonymized dataset is heavily incomplete.”

The Nature paper is actually about the probability for an individual to be uniquely identified from the given dataset, which somewhat different from the NYT headlines. Using a copula for the distribution of the covariates. And assessing the model with a mean square error evaluation when what matters are false positives and false negatives. Note that the model need be trained for each new dataset, which reduces the appeal of the claim, especially when considering that individuals tagged as uniquely identified about 6% are not. The statistic of 99.98% posted in the NYT is actually a count on a specific dataset,  the 5% Public Use Microdata Sample files, and Massachusetts residents, and not a general statistic [which would not make much sense!, as I can easily imagine 15 useless covariates] or prediction from the authors’ model. And a wee bit anticlimactic.

graph of the day & AI4good versus AI4bad

Apart from the above graph from Nature, rendering in a most appalling and meaningless way the uncertainty about the number of active genes in the human genome, I read a couple of articles in this issue of Nature relating to the biases and dangers of societal algorithms. One of which sounded very close to the editorial in the New York Times on which Kristian Lum commented on this blog. With the attached snippet on what is fair and unfair (or not).

The second article was more surprising as it defended the use of algorithms for more democracy. Nothing less. Written by Wendy Tam Cho, professor of political sciences, law, statistics, and mathematics at UIUC, it argued that the software that she develops to construct electoral maps produces fair maps. Which sounds over-rosy imho, as aiming to account for all social, ethnic, income, &tc., groups, i.e., most of the axes that define a human, is meaningless, if only because the structure of these groups is not frozen in time. To state that “computers are impervious to the lure of power” is borderline ridiculous, as computers and algorithms are [so far] driven by humans. This is not to say that gerrymandering should not be fought by technological means, especially and obviously by open source algorithms, as existing proposals (discussed here) demonstrate, but to entertain the notion of a perfectly representative redistricting is not only illusory, but also far from democratic as it shies away from the one person one vote  at the basis of democracy. And the paper leaves us on the dark as to whom will decide on which group or which characteristic need be represented in the votes. Of course, this is the impression obtained by reading a one page editorial in Nature [in an overcrowded and sweltering commuter train] rather than the relevant literature. Nonetheless, I remain puzzled at why this editorial was ever published. (Speaking of democracy, the issue contains also warning reports about Hungary’s ultra-right government taking over the Hungarian Academy of Sciences.)