the privacy fallacy [book review]

“The World changed significantly since 1973.” (p.10)

I read this book, The Privacy Fallacy: Harm and Power in the Information Economy, by Ignacio Cofone, upon my return from Warwick the past week. This is a Cambridge University Press 2023 book I had picked from their publication list after reviewing a book proposal for them. A selection made with our ERC OCEAN goals in mind, but without paying enough attention to the book table of contents, since it proved to be a Law book!

“People’s inability to assess privacy risks impact people’s behavior toward privacy because it turns the risks into uncertainty, a kind of risk that is impossible to estimate.” (p.31)

Still, this ended up being a fairly interesting read (for me) about the shortcomings of the current legal privacy laws (in various countries), since they are based on an obsolete perception that predates AIs and social media. Its main theme is that privacy is a social value that must be protected, regardless of whether or not its breach has tangible consequences. The author then argues that notions that support these laws such as the rationality of individual choices, the confusion between privacy and secrecy, the binary dichotomy between public and private, &tc., all are erroneous, hence the “fallacy” he denounces. One immediate argument for his position is the extreme imbalance of information between individuals and corporations, the former being unable to assess the whole impact of clicking on “I agree” when visiting a webpage or installing a new app. The more because the data thus gathered is pipelined to third parties. (“One’s efforts cannot scale to the number of corporations collecting and using one’s personal data”, p.93) For similar reasons, Cofone further states that the current principles based on contracts are inappropriate. Also because data harm can be collective and because companies have a strong incentive to data exploitation, hence a moral hazard.

“Inferences, relational data, and de-identified data aren’t captured by consent provisions.” (p.9)

“AI inferences worsen information overload (…) As [they] continue to grow, so will the insufficiency of our processing ability to estimate our losses.” (p.75)

As illustrated by the surrounding quotes, the statistical and machine-learning aspects of the book are few and vague, in that the additional level of privacy loss due to post-data processing is considered as a further argument for said loss to be impossible to quantify and assess, without a proper evaluation of the channels through which this can happen and without a reglementary proposal towards its control. This level of discourse makes AIs appear as omniscient methods, unfortunately.

“Inferences are invisible (…) Risks posed by inferences are impossible to anticipate because the information inferred is disproportionate to the sum of the information disclosed.” (p.49)

“The idea of probabilistic privacy loss is crucial in a world where entities (..) mostly affect our privacy by making inferences” (p.121)

The attempts at regulation such as opt-in and informed consent are then denounced as illusions—obviously so imho, even without considering the nuisance of having to click on “Reject” for each newly visited website!—. De- and re-identified data does not require anyone’s consent. Data protection rights, as of today, do not provide protection in most cases, the burden of proof residing on the privacy victims rather than the perpetrators. The book unsurprisingly offers no technical suggestion towards ensuring corporations and data brokers comply with this respect of privacy and on the opposite agrees that institutional attempts such as GDPR remain well-intended wishful thinking w/o imposing a hard-wired way of controlling the data flows, with the “need of an enforcement authority with investigating and sanctioning powers” (p.106) . The only in-depth proposal therein is pushing for stronger accountability of these corporations via a new type of liability, with a prospect of class actions (if only in countries with this judiciary possibility).

[Disclaimer about potential self-plagiarism: this post or an edited version will eventually appear in my Books Review section in CHANCE.]

thou shalt not slice thine spaghetti

This 2023 work on Slice sampler on manifolds, as presented in the algorithms seminar in Warwick during a recent visit of by Mareike Hasenpflug, consists in designing and validating slice samplers for distributions on manifolds. It is mildly connected to some current work on MCMC algorithms on manifolds through coupling techniques by [my friends & coauthors] Elena Bortolado, Pierre Jacob, and Robin Ryder (who escape temporarily the manifold at each step). As in Neal (2003), uniform draws from the (super)level sets are replaced there with one-step Markov moves within the level set,  that is, slice sampler moves.  The slice sampler actually generalises Neal’s (2003) stepping-out and shrinkage steps rather closely. Based on the standard notion of the Riemannian measure induced by the very structure of the manifold, the model therein assumes that the simulation target is available as a closed-form if unormalised density p(x) against that measure, meaning that problems where the distribution is a push-forward one induced by a mapping onto the manifold are not necessary manageable. The slide sampler is decomposed into choosing (1-dimensional) geodesics defined by the manifold (and generalising great circles), uniformly, and then sampling by this one-dimensional slice sampling over the geodesic, under the level set constraint. Meaning that those geodesics must be manageable enough. (Note that the concept of stepping-out does not mean that the chain ever escapes from the manifold.) Demonstrating the validity and reversibility proves a challenging task.

connection between tempering & entropic mirror descent

The next One World ABC webinar is this  Thursday,  the 2nd May, at 9am UK time, with Francesca Crucinio (King’s College London, formerly CREST and even more formerly Warwick) presenting

“A connection between Tempering and Entropic Mirror Descent”.

a joint work with Nicolas Chopin and Anna Korba (both from CREST) whose abstract follows:

This work explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormalized density is known. We establish that tempering SMC corresponds to entropic mirror descent applied to the reverse Kullback-Leibler (KL) divergence and obtain convergence rates for the tempering iterates. Our result motivates the tempering iterates from an optimization point of view, showing that tempering can be seen as a descent scheme of the KL divergence with respect to the Fisher-Rao geometry, in contrast to Langevin dynamics that perform descent of the KL with respect to the Wasserstein-2 geometry. We exploit the connection between tempering and mirror descent iterates to justify common practices in SMC and derive adaptive tempering rules that improve over other alternative benchmarks in the literature.

travel woes

On my last trip to Warwick, the local (RER) train I boarded broke on its way to the CDG airport, after hitting something in a tunnel just three stops short of the airport, with so much delay and misleading communication that I missed my flight. While a minor issue for me, since I managed to work (and blog) in an airport lounge for most of the day—where I crossed path with Numerobis—, while waiting for the only flight to B’ham, this made me to reflect anew on the very poor state of the transportation network in Paris and its suburbs, with such incidents (power failures, broken rails, vetust engines, stolen cables, idiots on the tracks, &tc., even without mentioning the strikes) more and more the norm. And to wonder at how the ancient and bursting network is going to cope with the incoming flow of visitors attending the Olympics this summer… Actually, when compared with the other cities with a fairly reasonably efficient airport connection I experienced, it remains a mystery to me why the Greater Paris conurbation—whose president, Valérie Pécresse, was apparently deemed the main culprit for our train break by an incensed fellow passenger that morning!—has kept for years postponing the construction of a dedicated rail line between the airport and central Paris, as the unpredictable and uncomfortable suburban train is not delivering the intended message to Paris visitors and their still-growing contribution to the French GNP. But with the growing public opposition to any new infrastructure, incl. trains, this is unlikely to happen!

warming stripes [but 1d]

Just recently, I came across this representation of the (global) temperature rise over the period 1850-2020, made by Professor Ed Hawkins from the University of Reading. It is powerful, artsy, reminding me of the Everything mural in the Zeeman Building at the University of Warwick, and visually most appealing, as a poster about the urgency for action, but it nonetheless remain a poor statistical graph in that it is a 2d presentation of a 1d dataset, the time series of the yearly average temperatures, missing the opportunity to exploit the second axis by eg taking a stripe of the Earth from South Pole to North Pole or the daily temperature throughout the year. And it is also lacking a scale, not only by omitting the dates on the first axis, but also by choosing an arbitrary if dramatic colour range [unable to incorporate the so-far ultimate 2023?!] (And with the visual appeal comes the danger of turning it into yet another marketing gimmick, hence cancelling its message.)