M2^2M: A general method to perform various data analysis tasks from a differentially private sketch

Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with reasonable accuracy, which makes it challenging to deploy in practice. This can be alleviated by private sketching, where the dataset is compressed into a single noisy sketch vector which can be shared with the analysts and used to perform arbitrarily many analyses. However, the algorithms to perform specific tasks from sketches must be developed on a case-by-case basis, which is a major impediment to their use. In this paper, we introduce the generic moment-to-moment (M$^2$M) method to perform a wide range of data exploration tasks from a single private sketch. Among other things, this method can be used to estimate empirical moments of attributes, the covariance matrix, counting queries (including histograms), and regression models. Our method treats the sketching mechanism as a black-box operation, and can thus be applied to a wide variety of sketches from the literature, widening their ranges of applications without further engineering or privacy loss, and removing some of the technical barriers to the wider adoption of sketches for data exploration under differential privacy. We validate our method with data exploration tasks on artificial and real-world data, and show that it can be used to reliably estimate statistics and train classification models from private sketches.Comment: Published at the 18th International Workshop on Security and Trust Management (STM 2022

Compressive Learning with Privacy Guarantees

International audienceThis work addresses the problem of learning from large collections of data with privacy guarantees. The compressive learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed. We show that a simple perturbation of this mechanism with additive noise is sufficient to satisfy differential privacy, a well established formalism for defining and quantifying the privacy of a random mechanism. We combine this with a feature subsampling mechanism, which reduces the computational cost without damaging privacy. The framework is applied to the tasks of Gaussian modeling, k-means clustering and principal component analysis (PCA), for which sharp privacy bounds are derived. Empirically, the quality (for subsequent learning) of the compressed representation produced by our mechanism is strongly related with the induced noise level, for which we give analytical expressions

Spontaneous product segregation from reactions in ionic liquids: application in Pd-catalyzed aliphatic alcohol oxidation

A methodology is introduced to separate polar reaction products from ionic liquids without the need for organic solvent extraction or distillation. We investigated product isolation after an alcohol oxidation performed in ionic liquids. Suitable ionic liquids were selected based on their mixing or demixing with a range of alcohols and the derived ketones. The aim was to obtain complete miscibility with the alcohol substrate at reaction temperature and a clear phase separation of the derived ketone product at room temperature. Six imidazolium based ionic liquids displayed this desired behaviour and were sufficiently stable to oxidation. These ionic liquids were then employed in the oxidation of non-activated aliphatic alcohols with molecular oxygen in the presence of palladium(II) acetate. In 1-butyl-3-methylimidazolium tetrafluoroborate, 2-ketone yields of 79 and 86% were obtained for, respectively, 2-octanol and 2-decanol. After cooling to room temperature the ionic liquid expels the immiscible ketone and the product phase can be isolated by decantation. In addition, the ionic liquid acts as an immobilization medium for the palladium catalyst, allowing efficient catalyst recycling.status: publishe

Compressive k-Means with Differential Privacy

In the compressive learning framework, one harshly compresses a whole training dataset into a single vector of generalized random moments, the sketch, from which a learning task can subsequently be performed. We prove that this loss of information can be leveraged to design a differentially private mechanism, and study empirically the privacy-utility tradeoff for the k-means clustering problem

Differentially Private Compressive K-means

This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed. We modify the standard sketching mechanism to provide differential privacy, using addition of Laplace noise combined with a subsampling mechanism (each moment is computed from a subset of the dataset). The data can be divided between several sensors, each applying the privacy-preserving mechanism locally, yielding a differentially-private sketch of the whole dataset when reunited. We apply this framework to the k-means clustering problem, for which a measure of utility of the mechanism in terms of a signal-to-noise ratio is provided, and discuss the obtained privacy-utility tradeoff

Time-Dependent density functional theory for spin dynamics

With the development of ultrashort sub-picosecond laser pulses, the last two decades have witnessed the emergence of a new field of magnetism, namely, femtomagnetism. This consists of controlling the magnetic interactions by using purely optical stimuli at sub-picosecond timescales, where both the exchange interaction and the magnetic anisotropy cannot be considered constant. The modeling of such phenomena is at present populated by semiempirical theories, which heavily rely on assumptions about the dominant interactions responsible for the dynamics and the system intrinsic properties (e.g., the conductivity). However, in the last few years, there have been a few attempts to look at the problem from a purely ab initio point of view, namely, by using time-dependent density functional theory. Here we will review the progress in this field and show how a theory not biased by assumptions can shed light into the fundamental aspects of the laser-induced magnetization dynamics. In particular we will discuss the ultrafast demagnetization of transition metals both in their cluster and bulk form and the possibility of spin transfer between sublattices in compounds containing magnetic ions. The chapter is also complemented by a short review of time-dependent spin density functional theory in the context of spin dynamics

Limited CD4+ T cell renewal in early stage of HIV-1 infection: effect of highly active antiretroviral therapy.

We show that the fraction of proliferating CD4+ lymphocytes is similar in HIV-infected subjects in the early stage of disease and in HIV-negative subjects, whereas the fraction of proliferating CD8+ lymphocytes is increased 6.8-fold in HIV-infected subjects. After initiation of antiviral therapy, there is a late increase in proliferating CD4+ T cells associated with the restoration of CD4+ T-cell counts. These results provide strong support for the idea of limited CD4+ T-cell renewal in the early stage of HIV infection and indicate that after effective suppression of virus replication, the mechanisms of CD4+ T-cell production are still functional in early HIV infection