Proceedings from the Synthetic LBD International Seminar

On May 9, 2017, we hosted a seminar to discuss the conditions necessary to im- plement the SynLBD approach with interested parties, with the goal of providing a straightforward toolkit to implement the same procedure on other data. The proceed- ings summarize the discussions during the workshop

Measuring Membership Privacy on Aggregate Location Time-Series

While location data is extremely valuable for various applications, disclosing it prompts serious threats to individuals' privacy. To limit such concerns, organizations often provide analysts with aggregate time-series that indicate, e.g., how many people are in a location at a time interval, rather than raw individual traces. In this paper, we perform a measurement study to understand Membership Inference Attacks (MIAs) on aggregate location time-series, where an adversary tries to infer whether a specific user contributed to the aggregates. We find that the volume of contributed data, as well as the regularity and particularity of users' mobility patterns, play a crucial role in the attack's success. We experiment with a wide range of defenses based on generalization, hiding, and perturbation, and evaluate their ability to thwart the attack vis-a-vis the utility loss they introduce for various mobility analytics tasks. Our results show that some defenses fail across the board, while others work for specific tasks on aggregate location time-series. For instance, suppressing small counts can be used for ranking hotspots, data generalization for forecasting traffic, hotspot discovery, and map inference, while sampling is effective for location labeling and anomaly detection when the dataset is sparse. Differentially private techniques provide reasonable accuracy only in very specific settings, e.g., discovering hotspots and forecasting their traffic, and more so when using weaker privacy notions like crowd-blending privacy. Overall, our measurements show that there does not exist a unique generic defense that can preserve the utility of the analytics for arbitrary applications, and provide useful insights regarding the disclosure of sanitized aggregate location time-series

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

The Nature of Starburst Activity in M82

We present new evolutionary synthesis models of M82 based mainly on observations consisting of near-infrared integral field spectroscopy and mid-infrared spectroscopy. The models incorporate stellar evolution, spectral synthesis, and photoionization modeling, and are optimized for 1-45 micron observations of starburst galaxies. The data allow us to model the starburst regions on scales as small as 25 pc. We investigate the initial mass function (IMF) of the stars and constrain quantitatively the spatial and temporal evolution of starburst activity in M82. We find a typical decay timescale for individual burst sites of a few million years. The data are consistent with the formation of very massive stars (> 50-100 Msun) and require a flattening of the starburst IMF below a few solar masses assuming a Salpeter slope at higher masses. Our results are well matched by a scenario in which the global starburst activity in M82 occurred in two successive episodes each lasting a few million years, peaking about 10 and 5 Myr ago. The first episode took place throughout the central regions of M82 and was particularly intense at the nucleus while the second episode occurred predominantly in a circumnuclear ring and along the stellar bar. We interpret this sequence as resulting from the gravitational interaction M82 and its neighbour M81, and subsequent bar-driven evolution. The short burst duration on all spatial scales indicates strong negative feedback effects of starburst activity, both locally and globally. Simple energetics considerations suggest the collective mechanical energy released by massive stars was able to rapidly inhibit star formation after the onset of each episode.Comment: 48 pages, incl. 16 Postscript figures; accepted for publication in the Astrophysical Journa