Examining the Limits of Predictability of Human Mobility

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of human-mobility on deriving this upper bound on maximum mobility predictability. By using several statistical tests on three real-world mobility datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, contrasting with the initial Markovian assumption. We show that this assumption of exponential decay of information in mobility trajectories, coupled with the inadequate usage of encoding techniques results in entropy inflation, consequently lowering the upper bound on predictability. We highlight that the current upper bound computation methodology based on Fano’s inequality tends to overlook the presence of long-range structural correlations inherent to mobility behaviors and we demonstrate its significance using an alternate encoding scheme. We further show the manifestation of not accounting for these dependencies by probing the mutual information decay in mobility trajectories. We expose the systematic bias that culminates into an inaccurate upper bound and further explain as to why the recurrent-neural architectures, designed to handle long-range structural correlations, surpass this upper limit on human mobility predictability

An Adaptive Algorithm for Efficient Message Diffusion in Unreliable Environments

In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic model, i.e., where links lose messages and where processes crash and recover probabilistically. Our approach consists in first defining the optimality of probabilistic reliable broadcast algorithms and the adaptiveness of algorithms that aim at converging toward such optimality. Then, we propose an algorithm that precisely converges toward the optimal behavior, thanks to an adaptive strategy based on Bayesian statistical inference. Our adaptive algorithm is modular and consists of two activities. The first activity is responsible for solving the reliable broadcast, given information about the failure probability of each link and of each process. This activity relies on the notion of Maximum Reliability Tree, which we derive from the notion of Maximum Spanning Tree. The other activity is responsible for approximating failure probabilities of links and processes, using Bayesian networks. We compare the performance of our algorithm with that of a typical gossip algorithm through simulation. Our results show, for example, that our adaptive algorithm quickly converges toward such exact knowledge

Know their Customers: An Empirical Study of Online Account Enumeration Attacks

Internet users possess accounts on dozens of online services where they are often identified by one of their e-mail addresses. They often use the same address on multiple services and for communicating with their contacts. In this paper, we investigate attacks that enable an adversary (e.g., company, friend) to determine (stealthily or not) whether an individual, identified by their e-mail address, has an account on certain services (i.e., an account enumeration attack). Such attacks on account privacy have serious implications as information about one’s accounts can be used to (1) profile them and (2) improve the effectiveness of phishing. We take a multifaceted approach and study these attacks through a combination of experiments (63 services), surveys (318 respondents), and focus groups (13 participants). We demonstrate the high vulnerability of popular services (93.7%) and the concerns of users about their account privacy, as well as their increased susceptibility to phishing e-mails that impersonate services on which they have an account. We also provide findings on the challenges in implementing countermeasures for service providers and on users’ ideas for enhancing their account privacy. Finally, our interaction with national data protection authorities led to the inclusion of recommendations in their developers’ guide

Frugal Mobile Objects

This paper presents a computing model for resource-limited mobile devices. The originality of the model lies in the integration of a strongly-typed event-based communication paradigm with abstractions for frugal control, assuming a small footprint runtime. With our model, an application consists of a set of distributed reactive objects, called FROBs, that communicate through typed events and dynamically adapt their behavior reacting to notifications typically based on resource availability. FROBs have a logical time-slicing execution pattern that helps monitor resource consuming tasks and determine resource profiles in terms of CPU, memory, and bandwidth. The behavior of a FROB is represented by a set of stateless first-class objects. Both state and behavioral objects are referenced through a level of indirection within the FROB. This facilitates the dynamic changes of the set of event types a FROB can accept, say based on the available resources, without requiring a significant footprint of the underlying FROB runtime. We demonstrate the usability of the FROB model through our Java-based prototype and a peer-to-peer audio streaming scenario where an audio provider dynamically adjusts its quality of service by adapting to demand. The performance results of our prototype convey the small footprint of our FROB runtime (86 kilobytes). We also augmented the KVM to enable resource profiling with however a negligible overhead (less than 0.5%) and a decrease in speed of the virtual machine of at most 7%

Breadcrumbs: A Rich Mobility Dataset with Point-of-Interest Annotations (short paper)

Rich human mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems. Unfortunately , existing mobility datasets-that are available to the research community-are restricted to location data captured through a single sensor (typically GPS) and have a low spatiotemporal granularity. They also lack ground-truth data regarding points of interest and the associated semantic labels (e.g., "home", "work", etc.). In this paper, we present Breadcrumbs, a rich mobility dataset collected from multiple sensors (incl. GPS, GSM, WiFi, Bluetooth) on the smartphones of 81 individuals. In addition to sensor data, Breadcrumbs contains ground-truth data regarding people points of interest (incl. semantic labels) as well as demographic attributes, contact records, calendar events, lifestyle information, and social relationship labels between the participants of the study. We describe the data collection methodology and present a preliminary quantitative analysis of the dataset. A sanitized version of the dataset as well as the source code will be made available to the research community

Abstract Using the Strategy Design Pattern to Compose Reliable Distributed Protocols

Reliable distributed systems involve many complex protocols. In this context, protocol composition is a central concept, because it allows the reuse of robust protocol implementations. In this paper, we describe how the Strategy pattern has been recursively used to support protocol composition in the BAST framework. We also discuss design alternatives that have been applied in other existing frameworks.