arXiv subjects

David Sánchez

Publications and source records attributed to David Sánchez.

At least 37 records · Page 2Linked to original sources

arXiv2021

Geometry effects in topologically confined bilayer graphene loops

We investigate the electronic confinement in bilayer graphene by topological loops of different shapes. These loops are created by lateral gates acting via gap inversion on the two graphene sheets. For large-area loops the spectrum is well described by a quantization rule depending only on the loop perimeter. For small sizes, the spectrum depends on the loop shape. We find that zero-energy states exhibit a characteristic pattern that strongly depends on the spatial symmetry. We show this by considering loops of higher to lower symmetry (circle, square, rectangle and irregular polygon). Interestingly, magnetic field causes valley splittings of the states, an asymmetry between energy reversal states, flux periodicities and the emergence of persistent currents.

cond-mat.mes-hall↗

arXiv2021

Scattering of topological kink-antikink states in bilayer graphene structures

Gapped bilayer graphene can support the presence of intragap states due to kink gate potentials applied to the graphene layers. Electrons in these states display valley-momentum locking, which makes them attractive for topological valleytronics. Here, we show that kink-antikink local potentials enable modulated scattering of topological currents. We find that the kink-antikink coupling leads to anomalous steps in the junction conductance. Further, when the constriction detaches from the propagating modes, forming a loop, the conductance reveals the system energy spectrum. Remarkably, these kink-antikink devices can also work as valley filters with tiny magnetic fields by tuning a central gate.

cond-mat.mes-hall↗

arXiv2021

Secure and Privacy-Preserving Federated Learning via Co-Utility

The decentralized nature of federated learning, that often leverages the power of edge devices, makes it vulnerable to attacks against privacy and security. The privacy risk for a peer is that the model update she computes on her private data may, when sent to the model manager, leak information on those private data. Even more obvious are security attacks, whereby one or several malicious peers return wrong model updates in order to disrupt the learning process and lead to a wrong model being learned. In this paper we build a federated learning framework that offers privacy to the participating peers as well as security against Byzantine and poisoning attacks. Our framework consists of several protocols that provide strong privacy to the participating peers via unlinkable anonymity and that are rationally sustainable based on the co-utility property. In other words, no rational party is interested in deviating from the proposed protocols. We leverage the notion of co-utility to build a decentralized co-utile reputation management system that provides incentives for parties to adhere to the protocols. Unlike privacy protection via differential privacy, our approach preserves the values of model updates and hence the accuracy of plain federated learning; unlike privacy protection via update aggregation, our approach preserves the ability to detect bad model updates while substantially reducing the computational overhead compared to methods based on homomorphic encryption.

cs.CR↗

arXiv2020

Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions

Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues. On the one hand, the model updates sent by the clients at each training epoch might leak information on the clients' private data. On the other hand, the model learnt by the server may be subjected to attacks by malicious clients; these security attacks might poison the model or prevent it from converging. In this paper, we first examine security and privacy attacks to FL and critically survey solutions proposed in the literature to mitigate each attack. Afterwards, we discuss the difficulty of simultaneously achieving security and privacy protection. Finally, we sketch ways to tackle this open problem and attain both security and privacy.

cs.CR↗

arXiv2020

The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)

Differential privacy (DP) is a neat privacy definition that can co-exist with certain well-defined data uses in the context of interactive queries. However, DP is neither a silver bullet for all privacy problems nor a replacement for all previous privacy models. In fact, extreme care should be exercised when trying to extend its use beyond the setting it was designed for. This paper reviews the limitations of DP and its misuse for individual data collection, individual data release, and machine learning.

cs.CR↗

arXiv2019

Fluctuation-driven Coulomb drag in interacting quantum dot systems

Coulomb drag between nanoscale conductors is of both fundamental and practical interest. Here, we theoretically study drag in a double quantum-dot (QD) system consisting of a biased drive QD and an unbiased drag QD coupled via a direct interdot Coulomb interaction. We demonstrate that the Coulomb drag is driven by the charge fluctuations in the drive QD, and show how the properties of the associated quantum noise allow to distinguish it from, e.g., shot-noise driven drag in circuits of weakly interacting quantum conductors. In the strong-interaction regime exhibiting an orbital ("pseudospin") Kondo effect, the drag is governed by charge fluctuations induced by pseudospin-flip cotunneling processes. The quenching of pseudospin-flip processes by Kondo correlations are found to suppress the drag at low bias and introduce a zero-bias anomaly in the second-order differential transconductance. Finally, we show that the drag is maximized for values of the interdot interaction matching the lead couplings. Our findings are relevant for the understanding of drag in QD systems and provide experimentally testable predictions in different transport regimes.

cond-mat.mes-hall↗

arXiv2019

Imaging the Thermal and Kinematic Sunyaev-Zel'dovich Effect Signals in a Sample of Ten Massive Galaxy Clusters: Constraints on Internal Velocity Structures and Bulk Velocities

We have imaged the Sunyaev-Zel'dovich (SZ) effect signals at 140 and 270 GHz towards ten galaxy clusters with Bolocam and AzTEC/ASTE. We also used Planck data to constrain the signal at large angular scales, Herschel-SPIRE images to subtract the brightest galaxies that comprise the cosmic infrared background (CIB), Chandra imaging to map the electron temperature $T_e$ of the intra-cluster medium (ICM), and HST imaging to derive models of each galaxy cluster's mass density. The galaxy clusters gravitationally lens the background CIB, which produced an on-average reduction in brightness towards the galaxy clusters' centers after the brightest galaxies were subtracted. We corrected for this deficit, which was between 5-25% of the 270 GHz SZ effect signal within $R_{2500}$. Using the SZ effect measurements, along with the X-ray constraint on $T_e$, we measured each galaxy cluster's average line of sight (LOS) velocity $v_z$ within $R_{2500}$, with a median per-cluster uncertainty of +-700 km/s. We found an ensemble-mean <$v_z$> of 430+-210 km/s, and an intrinsic cluster-to-cluster scatter $σ_{int}$ of 470+-340 km/s. We also obtained maps of $v_z$ over each galaxy cluster's face with an angular resolution of 70". All four galaxy clusters previously identified as having a merger oriented along the LOS showed an excess variance in these maps at a significance of 2-4$σ$, indicating an internal $v_z$ rms of $\gtrsim$1000 km/s. None of the six galaxy clusters previously identified as relaxed or plane of sky mergers showed any such excess variance.

astro-ph.CO↗

arXiv2018

Topologically non-trivial valley states in bilayer graphene quantum point contacts

We present measurements of quantized conductance in electrostatically induced quantum point contacts in bilayer graphene. The application of a perpendicular magnetic field leads to an intricate pattern of lifted and restored degeneracies with increasing field: at zero magnetic field the degeneracy of quantized one-dimensional subbands is four, because of a twofold spin and a twofold valley degeneracy. By switching on the magnetic field, the valley degeneracy is lifted. Due to the Berry curvature states from different valleys split linearly in magnetic field. In the quantum Hall regime fourfold degenerate conductance plateaus reemerge. During the adiabatic transition to the quantum Hall regime, levels from one valley shift by two in quantum number with respect to the other valley, forming an interweaving pattern that can be reproduced by numerical calculations.

cond-mat.mes-hall↗

arXiv2018

Mapping the Americanization of English in Space and Time

As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.

cs.CL↗

arXiv2018

How to distinguish between interacting and noninteracting molecules in tunnel junctions

Recent experiments demonstrate a temperature control of the electric conduction through a ferrocene-based molecular junction. Here we examine the results in view of determining means to distinguish between transport through single-particle molecular levels or via transport channels split by Coulomb repulsion. Both transport mechanisms are similar in molecular junctions given the similarities between molecular intralevel energies and the charging energy. We propose an experimentally testable way to identify the main transport process. By applying a magnetic field to the molecule, we observe that an interacting theory predicts a shift of the conductance resonances of the molecule whereas in the noninteracting case each resonance is split into two peaks. The interaction model works well in explaining our experimental results obtained in a ferrocene-based single-molecule junction, where the charge degeneracy peaks shift (but do not split) under the action of an applied 7-Tesla magnetic field. This method is useful for a proper characterization of the transport properties of molecular tunnel junctions.

cond-mat.mes-hall↗

arXiv2017

Privacy-preserving data outsourcing in the cloud via semantic data splitting

Even though cloud computing provides many intrinsic benefits, privacy concerns related to the lack of control over the storage and management of the outsourced data still prevent many customers from migrating to the cloud. Several privacy-protection mechanisms based on a prior encryption of the data to be outsourced have been proposed. Data encryption offers robust security, but at the cost of hampering the efficiency of the service and limiting the functionalities that can be applied over the (encrypted) data stored on cloud premises. Because both efficiency and functionality are crucial advantages of cloud computing, in this paper we aim at retaining them by proposing a privacy-protection mechanism that relies on splitting (clear) data, and on the distributed storage offered by the increasingly popular notion of multi-clouds. We propose a semantically-grounded data splitting mechanism that is able to automatically detect pieces of data that may cause privacy risks and split them on local premises, so that each chunk does not incur in those risks; then, chunks of clear data are independently stored into the separate locations of a multi-cloud, so that external entities cannot have access to the whole confidential data. Because partial data are stored in clear on cloud premises, outsourced functionalities are seamlessly and efficiently supported by just broadcasting queries to the different cloud locations. To enforce a robust privacy notion, our proposal relies on a privacy model that offers a priori privacy guarantees; to ensure its feasibility, we have designed heuristic algorithms that minimize the number of cloud storage locations we need; to show its potential and generality, we have applied it to the least structured and most challenging data type: plain textual documents.

cs.CR↗

arXiv2017

Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees

Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set. However, enforcing this strict guarantee in practice significantly distorts data and/or limits data uses, thus diminishing the analytical utility of the differentially private results. In an attempt to address this shortcoming, several relaxations of differential privacy have been proposed that trade off privacy guarantees for improved data utility. In this work, we argue that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks. In particular, the standard formalization requires indistinguishability of results between any pair of neighbor data sets, while indistinguishability between the actual data set and its neighbor data sets should be enough. This limits the data controller's ability to adjust the level of protection to the actual data, hence resulting in significant accuracy loss. In this respect, we propose individual differential privacy, an alternative differential privacy notion that offers em the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals). This new notion allows the data controller to adjust the distortion to the actual data set, which results in less distortion and more analytical accuracy. We propose several mechanisms to attain individual differential privacy and we compare the new notion against standard differential privacy in terms of the accuracy of the analytical results.

cs.CR↗

arXiv2017

Learning about Spanish dialects through Twitter

This paper maps the large-scale variation of the Spanish language by employing a corpus based on geographically tagged Twitter messages. Lexical dialects are extracted from an analysis of variants of tens of concepts. The resulting maps show linguistic variation on an unprecedented scale across the globe. We discuss the properties of the main dialects within a machine learning approach and find that varieties spoken in urban areas have an international character in contrast to country areas where dialects show a more regional uniformity.

stat.ML↗

arXiv2017

Toward sensitive document release with privacy guarantees

Privacy has become a serious concern for modern Information Societies. The sensitive nature of much of the data that are daily exchanged or released to untrusted parties requires that responsible organizations undertake appropriate privacy protection measures. Nowadays, much of these data are texts (e.g., emails, messages posted in social media, healthcare outcomes, etc.) that, because of their unstructured and semantic nature, constitute a challenge for automatic data protection methods. In fact, textual documents are usually protected manually, in a process known as document redaction or sanitization. To do so, human experts identify sensitive terms (i.e., terms that may reveal identities and/or confidential information) and protect them accordingly (e.g., via removal or, preferably, generalization). To relieve experts from this burdensome task, in a previous work we introduced the theoretical basis of C-sanitization, an inherently semantic privacy model that provides the basis to the development of automatic document redaction/sanitization algorithms and offers clear and a priori privacy guarantees on data protection; even though its potential benefits C-sanitization still presents some limitations when applied to practice (mainly regarding flexibility, efficiency and accuracy). In this paper, we propose a new more flexible model, named (C, g(C))-sanitization, which enables an intuitive configuration of the trade-off between the desired level of protection (i.e., controlled information disclosure) and the preservation of the utility of the protected data (i.e., amount of semantics to be preserved). Moreover, we also present a set of technical solutions and algorithms that provide an efficient and scalable implementation of the model and improve its practical accuracy, as we also illustrate through empirical experiments.

cs.CR↗

arXiv2016

Coulomb-blockade effect in nonlinear mesoscopic capacitors

We consider an interacting quantum dot working as a coherent source of single electrons. The dot is tunnel coupled to a reservoir and capacitively coupled to a gate terminal with an applied ac potential. At low frequencies, this is the quantum analog of the RC circuit with a purely dynamical response. We investigate the quantized dynamics as a consequence of ac pulses with large amplitude. Within a Keldysh-Green function formalism we derive the time-dependent current in the Coulomb blockade regime. Our theory thus extends previous models that considered either noninteracting electrons in nonlinear response or interacting electrons in the linear regime. We prove that the electron emission and absorption resonances undergo a splitting when the charging energy is larger than the tunnel broadening. For very large charging energies, the additional peaks collapse and the original resonances are recovered, though with a reduced amplitude. Quantization of the charge emitted by the capacitor is reduced due to Coulomb repulsion and additional plateaus arise. Additionally, we discuss the differential capacitance and resistance as a function of time. We find that to leading order in driving frequency the current can be expressed as a weighted sum of noninteracting currents shifted by the charging energy.

cond-mat.mes-hall↗

arXiv2016

Cotunneling drag effect in Coulomb-coupled quantum dots

In Coulomb drag, a current flowing in one conductor can induce a voltage across an adjacent conductor via the Coulomb interaction. The mechanisms yielding drag effects are not always understood, even though drag effects are sufficiently general to be seen in many low-dimensional systems. In this Letter, we observe Coulomb drag in a Coulomb-coupled double quantum dot (CC-DQD) and, through both experimental and theoretical arguments, identify cotunneling as essential to obtaining a correct qualitative understanding of the drag behavior.

cond-mat.mes-hall↗

arXiv2016

Privacy-driven Access Control in Social Networks by Means of Automatic Semantic Annotation

In online social networks (OSN), users quite usually disclose sensitive information about themselves by publishing messages. At the same time, they are (in many cases) unable to properly manage the access to this sensitive information due to the following issues: i) the rigidness of the access control mechanism implemented by the OSN, and ii) many users lack of technical knowledge about data privacy and access control. To tackle these limitations, in this paper, we propose a dynamic, transparent and privacy-driven access control mechanism for textual messages published in OSNs. The notion of privacy-driven is achieved by analyzing the semantics of the messages to be published and, according to that, assessing the degree of sensitiveness of their contents. For this purpose, the proposed system relies on an automatic semantic annotation mechanism that, by using knowledge bases and linguistic tools, is able to associate a meaning to the information to be published. By means of this annotation, our mechanism automatically detects the information that is sensitive according to the privacy requirements of the publisher of data, with regard to the type of reader that may access such data. Finally, our access control mechanism automatically creates sanitized versions of the users' publications according to the type of reader that accesses them. As a result, our proposal, which can be integrated in already existing social networks, provides an automatic, seamless and content-driven protection of user publications, which are coherent with her privacy requirements and the type of readers that access them. Complementary to the system design, we also discuss the feasibility of the system by illustrating it through a real example and evaluate its accuracy and effectiveness over standard approaches.

cs.SI↗

arXiv2016

Enforcing transparent access to private content in social networks by means of automatic sanitization

Social networks have become an essential meeting point for millions of individuals willing to publish and consume huge quantities of heterogeneous information. Some studies have shown that the data published in these platforms may contain sensitive personal information and that external entities can gather and exploit this knowledge for their own benefit. Even though some methods to preserve the privacy of social networks users have been proposed, they generally apply rigid access control measures to the protected content and, even worse, they do not enable the users to understand which contents are sensitive. Last but not least, most of them require the collaboration of social network operators or they fail to provide a practical solution capable of working with well-known and already deployed social platforms. In this paper, we propose a new scheme that addresses all these issues. The new system is envisaged as an independent piece of software that does not depend on the social network in use and that can be transparently applied to most existing ones. According to a set of privacy requirements intuitively defined by the users of a social network, the proposed scheme is able to: (i) automatically detect sensitive data in users' publications; (ii) construct sanitized versions of such data; and (iii) provide privacy-preserving transparent access to sensitive contents by disclosing more or less information to readers according to their credentials toward the owner of the publications. We also study the applicability of the proposed system in general and illustrate its behavior in two case studies.

cs.CR↗