Optimal Geo-Indistinguishable Mechanisms for Location Privacy

We consider the geo-indistinguishability approach to location privacy, and the trade-off with respect to utility. We show that, given a desired degree of geo-indistinguishability, it is possible to construct a mechanism that minimizes the service quality loss, using linear programming techniques. In addition we show that, under certain conditions, such mechanism also provides optimal privacy in the sense of Shokri et al. Furthermore, we propose a method to reduce the number of constraints of the linear program from cubic to quadratic, maintaining the privacy guarantees and without affecting significantly the utility of the generated mechanism. This reduces considerably the time required to solve the linear program, thus enlarging significantly the location sets for which the optimal mechanisms can be computed.Comment: 13 page

First record of two hard coral species (Faviidae and Siderastreidae) from Qeshm Island (Persian Gulf, Iran)

Abstrak. Moradi M, Kamrani E, Shokri MR, Ranjbar MS, Hesni MA (2009) Rekaman pertama dua spesies karang keras (Faviidae dan Siderastreidae) dari Pulau Qeshm (Teluk Persia, Iran). Nusantara Bioscience 2: 34-37. Dua jenis karang keras termasuk Cyphastrea chalcidicum (Forskal 1775) (Faviidae) dan Coscinaraea monile (Forskal 1775) (Siderastreidae) dikumpulkan dari selatan Pulau Qeshm (Teluk Persia, Iran) pada akhir tahun 2008. Spesies ini sebelumnya dilaporkan terdapat di Teluk Persia selatan, Teluk Aden, Afrika Tenggara dan Indo-Pasifik. Tinjauan literatur pada distribusi kedua jenis mengungkapkan bahwa spesies ini pertama kali tercatat dari Teluk Persia. Temuan ini semakin menunjukkan tingginya keragaman fauna karang di perairan Iran di bagian utara Teluk Persia. Kata kunci: catatan pertama, Coscinaraea monile, Cyphastrea chalcidicum, Qeshm island, Persian gulf

Privacy-Preserving Distance Computation and Proximity Testing on Earth, Done Right

In recent years, the availability of GPS-enabled smartphones have made location-based services extremely popular. A multitude of applications rely on location information to provide a wide range of services. Location information is, however, extremely sensitive and can be easily abused. In this paper, we introduce the first protocols for secure computation of distance and for proximity testing over a sphere. Our secure distance protocols allow two parties, Alice and Bob, to determine their mutual distance without disclosing any additional information about their location. Through our secure proximity testing protocols, Alice only learns if Bob is in close proximity, i.e., within some arbitrary distance. Our techniques rely on three different representations of Earth, which provide different trade-os between accuracy and performance. We show, via experiments on a prototype implementation, that our protocols are practical on resource- constrained smartphone devices. Our distance computation protocols runs, in fact, in 54 to 78 ms on a commodity Android smartphone. Similarly, our proximity tests require between 1.2 s and 2.8 s on the same platform. The imprecision introduced by our protocols is very small, i.e., between 0.1% and 3% on average, depending on the distance

Nyquist method for Wigner-Poisson quantum plasmas

By means of the Nyquist method, we investigate the linear stability of electrostatic waves in homogeneous equilibria of quantum plasmas described by the Wigner-Poisson system. We show that, unlike the classical Vlasov-Poisson system, the Wigner-Poisson case does not necessarily possess a Penrose functional determining its linear stability properties. The Nyquist method is then applied to a two-stream distribution, for which we obtain an exact, necessary and sufficient condition for linear stability, as well as to a bump-in-tail equilibrium.Comment: 6 figure

Disulfide-induced self-assembled targets:A novel strategy for the label free colorimetric detection of DNAs/RNAs via unmodified gold nanoparticles

A modified non-cross-linking gold-nanoparticles (Au-NPs) aggregation strategy has been developed for the label free colorimetric detection of DNAs/RNAs based on self-assembling target species in the presence of thiolated probes. Two complementary thiol-modified probes, each of which specifically binds at one half of the target introduced SH groups at both ends of dsDNA. Continuous disulfide bond formation at 3' and 5' terminals of targets leads to the self-assembly of dsDNAs into the sulfur-rich and flexible products with different lengths. These products have a high affinity for the surface of Au-NPs and efficiently protect the surface from salt induced aggregation. To evaluate the assay efficacy, a small part of the citrus tristeza virus (CTV) genome was targeted, leading to a detection limit of about 5 x 10(-9) mol. L-1 over a linear ranged from 20 x 10(-9) to 10 x 10(-7) mol. L-1. This approach also exhibits good reproducibility and recovery levels in the presence of plant total RNA or human plasma total circulating RNA extracts. Self-assembled targets can be then sensitively distinguished from non-assembled or mismatched targets after gel electrophoresis. The disulfide reaction method and integrating self-assembled DNAs/RNAs targets with bare AuNPs as a sensitive indicator provide us a powerful and simple visual detection tool for a wide range of applications

Analysis of a model for foam improved oil recovery

During improved oil recovery (IOR), gas may be introduced into a porous reservoir filled with surfactant solution in order to form foam. A model for the evolution of the resulting foam front known as ‘pressure-driven growth’ is analysed. An asymptotic solution of this model for long times is derived that shows that foam can propagate indefinitely into the reservoir without gravity override. Moreover, ‘pressure-driven growth’ is shown to correspond to a special case of the more general ‘viscous froth’ model. In particular, it is a singular limit of the viscous froth, corresponding to the elimination of a surface tension term, permitting sharp corners and kinks in the predicted shape of the front. Sharp corners tend to develop from concave regions of the front. The principal solution of interest has a convex front, however, so that although this solution itself has no sharp corners (except for some kinks that develop spuriously owing to errors in a numerical scheme), it is found nevertheless to exhibit milder singularities in front curvature, as the long-time asymptotic analytical solution makes clear. Numerical schemes for the evolving front shape which perform robustly (avoiding the development of spurious kinks) are also developed. Generalisations of this solution to geologically heterogeneous reservoirs should exhibit concavities and/or sharp corner singularities as an inherent part of their evolution: propagation of fronts containing such ‘inherent’ singularities can be readily incorporated into these numerical schemes

A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they actually expect to disclose, hence their privacy is threatened. To mitigate such threats, several privacy protection techniques based on location generalization have been proposed. Although the privacy implications of such techniques have been extensively studied, the utility implications are mostly unknown. In this paper, we propose a predictive model for quantifying the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. We first study the users’ motivations behind their location check-ins, based on a study targeted at Foursquare users (N = 77). We propose a machine-learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for the utility implications of generalization. Based on the survey data, our results show that the model accurately predicts the fine-grained motivation behind a check-in in 43% of the cases and in 63% of the cases for the coarse-grained motivation. It also predicts, with a mean error of 0.52 (on a scale from 1 to 5), the loss of utility caused by semantic and geographical generalization. This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service providers to implement location-sharing mechanisms that preserve both the utility and privacy for their users