Gaussian maximally multipartite entangled states

We study maximally multipartite entangled states in the context of Gaussian continuous variable quantum systems. By considering multimode Gaussian states with constrained energy, we show that perfect maximally multipartite entangled states, which exhibit the maximum amount of bipartite entanglement for all bipartitions, only exist for systems containing n=2 or 3 modes. We further numerically investigate the structure of these states and their frustration for n<=7.Comment: 6 pages, 2 figures, comments are welcom

No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone

It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis

Entanglement frustration in multimode Gaussian states

Bipartite entanglement between two parties of a composite quantum system can be quantified in terms of the purity of one party and there always exists a pure state of the total system that maximizes it (and minimizes purity). When many different bipartitions are considered, the requirement that purity be minimal for all bipartitions gives rise to the phenomenon of entanglement frustration. This feature, observed in quantum systems with both discrete and continuous variables, can be studied by means of a suitable cost function whose minimizers are the maximally multipartite-entangled states (MMES). In this paper we extend the analysis of multipartite entanglement frustration of Gaussian states in multimode bosonic systems. We derive bounds on the frustration, under the constraint of finite mean energy, in the low and high energy limit.Comment: 4 pages, 2 figures. Contribution to "Folding and Unfolding: Interactions from Geometry. Workshop in honour of Giuseppe Marmo's 65th birthday", 8-12 June 2011, Ischia (NA) Ital

No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position

News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.Comment: 14 pages, 9th International Conference on Network and System Security (NSS 2015

Vortex pinning and flux flow microwave studies of coated conductors

Demanding microwave applications in a magnetic field require the material optimization not only in zero-field but, more important, in the in-field flux motion dominated regime. However, the effect of artificial pinning centers (APC) remains unclear at high frequency. Moreover, in coated conductors the evaluation of the high frequency material properties is difficult due to the complicated electromagnetic problem of a thin superconducting film on a buffered metal substrate. In this paper we present an experimental study at 48 GHz of 150-200 nm YBa$_2$Cu$_3$O$_{7-x}$ coated conductors, with and without APCs, on buffered Ni-5at%W tapes. By properly addressing the electromagnetic problem of the extraction of the superconductor parameters from the measured overall surface impedance $Z$, we are able to extract and to comment on the London penetration depth, the flux flow resistivity and the pinning constant, highlighting the effect of artificial pinning centers in these samples.Comment: 5 pages, IEEE Trans. Appl. Supercond., accepted for publication (2019

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights

Single-atom-resolved probing of lattice gases in momentum space

Measuring the full distribution of individual particles is of fundamental importance to characterize many-body quantum systems through correlation functions at any order. Here we demonstrate the possibility to reconstruct the momentum-space distribution of three-dimensional interacting lattice gases atom-by-atom. This is achieved by detecting individual metastable Helium atoms in the far-field regime of expansion, when released from an optical lattice. We benchmark our technique with Quantum Monte-Carlo calculations, demonstrating the ability to resolve momentum distributions of superfluids occupying $10^5$ lattice sites. It permits a direct measure of the condensed fraction across phase transitions, as we illustrate on the superfluid-to-normal transition. Our single-atom-resolved approach opens a new route to investigate interacting lattice gases through momentum correlations.Comment: 7 pages, 5 figure

Effect of concrete tensile strength in non linear analyses of 2D structures - a comparison between three commercial finite element softwares

Non-linear finite element method (FEM) allows to take into account material and geometrical non-linearities in the simulation of the behaviour of reinforced concrete structures. However, the accuracy of the numerical solution with respect to experimental tests is often questionable, especially in the case of 2D and 3D structures. Several competitions showed in the past significant scatter of the predicted results with respect to the correct ones. Even though internationally well-known computer softwares can be used to predict the structural response, the uncertainty of the numerical simulation cannot be neglected. Therefore, the application of finite element models to the assessment of concrete structures requires a proper investigation of the uncertainty related to the results of the simulations. This paper presents a comparison of numerical simulations of sixteen case studies taken from past experimental tests and modelled with three commercial non-linear softwares. The purpose of the investigation is to show how significant could be the difference between the experimental and numerically evaluated failure load and displacement in function of the code used and the variation of only one material parameter

Aspects of geodesical motion with Fisher-Rao metric: classical and quantum

The purpose of this article is to exploit the geometric structure of Quantum Mechanics and of statistical manifolds to study the qualitative effect that the quantum properties have in the statistical description of a system. We show that the end points of geodesics in the classical setting coincide with the probability distributions that minimise Shannon's Entropy, i.e. with distributions of zero dispersion. In the quantum setting this happens only for particular initial conditions, which in turn correspond to classical submanifolds. This result can be interpreted as a geometric manifestation of the uncertainty principle.Comment: 15 pages, 5 figure