Detecting Communities under Differential Privacy

Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified their outperformance over the state-of-the-art

Anonymizing Social Graphs via Uncertainty Semantics

Rather than anonymizing social graphs by generalizing them to super nodes/edges or adding/removing nodes and edges to satisfy given privacy parameters, recent methods exploit the semantics of uncertain graphs to achieve privacy protection of participating entities and their relationship. These techniques anonymize a deterministic graph by converting it into an uncertain form. In this paper, we propose a generalized obfuscation model based on uncertain adjacency matrices that keep expected node degrees equal to those in the unanonymized graph. We analyze two recently proposed schemes and show their fitting into the model. We also point out disadvantages in each method and present several elegant techniques to fill the gap between them. Finally, to support fair comparisons, we develop a new tradeoff quantifying framework by leveraging the concept of incorrectness in location privacy research. Experiments on large social graphs demonstrate the effectiveness of our schemes

Pedal towards Safety: The Development and Evaluation of a Risk Index for Cyclists †

Cyclists are at a higher risk of being involved in accidents. To this end, a safer environment for cyclists should be pursued so that they can feel safe while riding their bicycles. Focusing on safety risks that cyclists may face is the main key to preserving safe mobility, reducing accidents, and improving their level of safety during their travel. Identifying and assessing risk factors, as well as informing cyclists about them may lead to an efficient and integrated transportation system. Therefore, the purpose of this research is to introduce a risk index that can be adapted to different road areas in order to measure the degree of how risky these areas are for biking. Cyclists’ behavior and demographics were integrated into the risk index calculation. The methodology followed to obtain the risk index composed of four phases: risk factor identification, risk factor weighting, risk index formulation, and risk index validation. Nineteen risk factors are categorized into four major groups: facility features, infrastructure features, cyclist behavior, and weather and traffic conditions

Handling Disturbance and Awareness of Concurrent Updates in a Collaborative Editor

International audienceWhen people work collaboratively on a shared document, they have two contradictory requirements on their editors that may affect the efficiency of their work. On the one hand, they would like to know what other people are currently doing on a particular part of the document. On the other hand, they would like to focus their attention on their own current work, with as little disturbance from the concurrent activities as possible. We present some features that help the user handle disturbance and awareness of concurrent updates. While collabora-tively editing a shared document with other people, a user can create a focus region. The user can concentrate on the work in the region without being interfered with the concurrent updates of the other people. Occasionally, the user can preview the concurrent updates and select a number of these updates to be integrated into the local copy. We have implemented a collaborative editing subsystem in the GNU Emacs 5 text editor with the described features

Identification of heavy vehicle parameters and impact forces estimation : experimental results

The aim of the presented work is to estimate the vertical forces of heavy vehicle and identify the unknown dynamic parameters using sliding mode observers approach. The vertical forces are then considered as perturbation to be identified. The observation needs a good knowledge of some dynamic parameters such as damping coefficient, spring stiffness...etc. In this paper, some of these parameters, namely spring stiffness and unsprung masses have been identified. Real time tests have been done on an instrumented vehicle which is equipped with different sensors in order to measure its dynamics. In order to show the quality of the estimation and identification, the estimation and identification results are then compared to the measures and presented in this paper

Adaptive Observers and Estimation of the Road Profile

SAE Transactions 2003International audienceno abstrac

Road profile inputs for evaluation of the loads on the wheels

International audienceno abstrac

Adaptive Observers and Estimation of the Road Profile

SAE Transactions 2003International audienceno abstrac

Identification of heavy vehicle parameters and impact forces estimation : experimental results

The aim of the presented work is to estimate the vertical forces of heavy vehicle and identify the unknown dynamic parameters using sliding mode observers approach. The vertical forces are then considered as perturbation to be identified. The observation needs a good knowledge of some dynamic parameters such as damping coefficient, spring stiffness...etc. In this paper, some of these parameters, namely spring stiffness and unsprung masses have been identified. Real time tests have been done on an instrumented vehicle which is equipped with different sensors in order to measure its dynamics. In order to show the quality of the estimation and identification, the estimation and identification results are then compared to the measures and presented in this paper

Sliding Mode Observers for Systems with Unknown Inputs: Application to estimate the Road Profile

International audienceIn this paper, a sliding-mode observer for systems with unknown inputs is presented. The system considered is a vehicle model with unknown inputs that represent the road profile variations. Coefficients of road adhesion are considered as unknown parameters. The tyre-road friction depends essentially on these parameters. The developed observer permits these longitudinal forces acting on the wheels to be estimated. Then another observer is developed to estimate the unknown inputs. In the first part of this work, some results are presented which are related to the validation of a full-car modelization, by means of comparisons between simulation results and experimental measurements (from a Peugeot 406 as a test car)