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Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users' devices. These transformations aim at eliminating patterns that can be used for user re-identification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data., Accepted to appear in Pervasive and Mobile computing (PMC) Journal, Elsevier

Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

There is growing concern about how personal data are used when users grant applications direct access to the sensors of their mobile devices. In fact, high resolution temporal data generated by motion sensors reflect directly the activities of a user and indirectly physical and demographic attributes. In this paper, we propose a feature learning architecture for mobile devices that provides flexible and negotiable privacy-preserving sensor data transmission by appropriately transforming raw sensor data. The objective is to move from the current binary setting of granting or not permission to an application, toward a model that allows users to grant each application permission over a limited range of inferences according to the provided services. The internal structure of each component of the proposed architecture can be flexibly changed and the trade-off between privacy and utility can be negotiated between the constraints of the user and the underlying application. We validated the proposed architecture in an activity recognition application using two real-world datasets, with the objective of recognizing an activity without disclosing gender as an example of private information. Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50\%, the target random guess, from more than 90\% when using raw sensor data. We also present and distribute MotionSense, a new dataset for activity and attribute recognition collected from motion sensors., 6 pages

Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%., Comment: 10 pages

Researchers from different disciplines have examined crowd behavior in the past by employing a variety of methods including ethnographic studies, computer vision techniques and manual annotation based data analysis. However, because of the inherent difficulties in collecting, processing and analyzing the data, it is difficult to obtain large data sets for study. In this work we present a system for detecting stationary interactions inside crowds, depending entirely on the sensors available in a modern smartphone device such as Bluetooth Smart (BLE) and Accelerometer. By utilizing Apple's iBeaconTM implementation of Bluetooth Smart using SensingKit1, our open-source multi-platform mobile sensing framework [1], we are able to detect the proximity of users carrying a smartphone in their pocket. We then use an algorithm based on graph theory to predict group interactions inside the crowd. Previous work in this area has been limited to the detection of interactions between only two people and therefore our approach goes beyond current state of the art in its ability to detect group formations with more than two people involved. Our approach is particularly beneficial to the design and implementation of crowd behavior analytics, design of influence strategies, and algorithms for crowd reconfiguration.

This paper addresses the problem of provisioning management/monitoring nodes within highly dynamic network environments, particularly virtual networks. In a network, where nodes and links may be spontaneously created and destroyed (perhaps rapidly) there is a need for stable and responsive management and monitoring, which does not create a large load (in terms of traffic or processing) for the system. A subset of nodes has to be chosen for management/monitoring, each of which will manage a subset of the nodes in the network. A new, simple, and locally optimal greedy algorithm called Pressure is provided for choice of node position to minimize traffic. This algorithm is combined with a system for predicting the lifespan of nodes, and a tunable parameter is also given so that a system operator could express a preference for elected nodes to be chosen to reduce traffic, to be "stable,” or some compromise between these positions. The combined algorithm called PressureTime is lightweight and could be run in a distributed manner. The resulting algorithms are tested both in simulation and in a testbed environment of virtual routers. They perform well, both at reducing traffic and at choosing long lifespan nodes.

Multi-user video conferencing is a ubiquitous technology. Increasingly end-hosts in a conference are assisted by cloud-based servers that improve the quality of experience for end users. This paper evaluates the impact of strategies for placement of such servers on user experience and deployment cost. We consider scenarios based upon the Amazon EC2 infrastructure as well as future scenarios in which cloud instances can be located at a larger number of possible sites across the planet. We compare a number of possible strategies for choosing which cloud locations should host services and how traffic should route through them. Our study is driven by real data to create demand scenarios with realistic geographical user distributions and diurnal behaviour. We conclude that on the EC2 infrastructure a well chosen static selection of servers performs well but as more cloud locations are available a dynamic choice of servers becomes important.

Researchers have examined crowd behavior in the past by employing a variety of methods including ethnographic studies, computer vision techniques and manual annotation based data analysis. However, because of the resources to collect, process and analyze data, it remains difficult to obtain large data sets for study. In an attempt to alleviate this problem, researchers have recently used mobile sensing, however this technique is currently only able to detect either stationary or moving crowds with questionable accuracy. In this work we present a system for detecting stationary interactions inside crowds using the Received Signal Strength Indicator of Bluetooth Smart (BLE) sensor, combined with the Motion Activity of each device. By utilizing Apple's iBeacon™ implementation of Bluetooth Smart, we are able to detect the proximity of users carrying a smartphone in their pocket. We then use an algorithm based on graph theory to predict interactions inside the crowd and verify our findings using video footage as ground truth. Our approach is particularly beneficial to the design and implementation of crowd behavior analytics, design of influence strategies, and algorithms for crowd reconfiguration.

This paper deals with the problem of assessing probabilistic models that represent the evolution of a target graph. Such models have long been a topic of interest for a number of networks, especially communications networks. The solution developed in this paper gives a rigorous way to calculate the likelihood of the observed graph evolution having arisen from a wide variety of hypothesized models encompassing many already present in the literature. The framework is shown to recover parameters from artificial data and is tested on real data sets from Facebook and from emails from the company Enron.

We present a system for streaming live entertainment content over the Internet originating from a single source to a scalable number of consumers without resorting to centralised or provider- provisioned resources. The system creates a peer-to-peer overlay network, which attempts to optimise use of existing capacity to ensure quality of service, delivering low start-up delay and lag in playout of the live content. There are three main aspects of our solution. Firstly, a swarming mechanism that constructs an overlay topology for minimising propagation delays from the source to end consumers. Secondly, a distributed overlay anycast system that uses a location-based search algorithm for peers to quickly find the closest peers in a given stream. Finally, a novel incentives mechanism that encourages peers to donate capacity even when the user is not actively consuming content., Comment: 11 pages, 6 figures

Eventual gait synchronization between two individuals while walking and talking with each other has been shown to be an indicator of agreeableness and companionship. The inferred physical signal from this subconscious phenomenon can potentially be an indicator of cooperation or relation between two individuals. In this paper we investigate this effect, and whether having a third person actively engaging in the same act or conversation can reduce this synchronization level. Using high frequency accelerometer data from a dedicated smartphone app, we perform a number of controlled experiments on a number of individuals in different group configuration. Our results bring an interesting insight: it is the non-verbal social signals such as the gaze, head orientation and gestures that is the key factor in synchronization, not necessarily the number or configuration of the walkers. These early results can lead us on detecting relationships between individuals or detecting the group formation and numbers for crowd-sensing applications when only partial data is available.

This paper describes TARDIS (Traffic Assignment and Retiming Dynamics with Inherent Stability) which is an algorithmic procedure designed to reallocate traffic within Internet Service Provider (ISP) networks. Recent work has investigated the idea of shifting traffic in time (from peak to off-peak) or in space (by using different links). This work gives a unified scheme for both time and space shifting to reduce costs. Particular attention is given to the commonly used 95th percentile pricing scheme. The work has three main innovations: firstly, introducing the Shapley Gradient, a way of comparing traffic pricing between different links at different times of day; secondly, a unified way of reallocating traffic in time and/or in space; thirdly, a continuous approximation to this system is proved to be stable. A trace-driven investigation using data from two service providers shows that the algorithm can create large savings in transit costs even when only small proportions of the traffic can be shifted.

This paper presents a statistically sound method for using likelihood to assess potential models of network evolution. The method is tested on data from five real networks. Data from the internet autonomous system network, from two photo sharing sites and from a co-authorship network are tested using this framework., Presented at ACM/SIMPLEX '09 Workshop, ACM/SIMPLEX '09 Proceedings of the 1st Annual Workshop on Simplifying Complex Network for Practitioners 2009

Many researchers have hypothesised models which explain the evolution of the topology of a target network. The framework described in this paper gives the likelihood that the target network arose from the hypothesised model. This allows rival hypothesised models to be compared for their ability to explain the target network. A null model (of random evolution) is proposed as a baseline for comparison. The framework also considers models made from linear combinations of model components. A method is given for the automatic optimisation of component weights. The framework is tested on simulated networks with known parameters and also on real data., Comment: Published in INFOCOM NetSciCom Workshop 2009. http://dl.acm.org/citation.cfm?id=1719896

The increasing demand for efficient content distribution using the Internet has fuelled the deployment of varied techniques such as peer-to-peer overlays, content distribution networks and distributed caching systems. These have had considerable impact on ISP infrastructure demand, motivating the development of protocols that enable mutually beneficial cooperative outcomes. In this paper we propose a parameterised cooperation utility that can be used to study the tradeoff between the benefit that an overlay obtains from the ISPs that carry its traffic and the costs that it imposes on them. With this utility, we find a closed-form expression for the optimal resource allocation given a particular cooperation tradeoff, subject to both minimal benefit and maximal cost constraints. Since the model is implementation-independent and has very modest computational demands, it is ideal for large scale simulation. We explore the properties of the proposed model through simulation in both a simple illustrative scenario and a more complete one based on network datasets. The results obtained from the model are shown to be consistent with those of measurement-based studies of overlay-ISP collaboration.

Although direct reciprocity (Tit-for-Tat )c ontribu- tion systems have been successful in reducing freeloading in peer- to-peer overlays, it has been shown that, unless the contribution network is dense, they tend to be slow (or may even fail) to converge (1). On the other hand, current indirect reciprocity mechanisms based on reputation systems tend to be susceptible to sybil attacks, peer slander and whitewashing. In this paper we present PledgeRoute, an accounting mech- anism for peer contributions that is based on social capital. This mechanism allows peers to contribute resources to one set of peers and use this contribution to obtain services from a different set of peers, at a different time. PledgeRoute is completely decentralised, can be implemented in both structured and unstructured peer-to-peer systems, and it is resistant to the three kinds of attacks mentioned above. To achieve this, we model contribution transitivity as a routing problem in the contribution network of the peer-to-peer overlay, and we present arguments for the routing behaviour and the sybilproofness of our contribution transfer procedures on this basis. Additionally, we present mechanisms for the seeding of the contribution network, and a combination of incentive mechanisms and reciprocation policies that motivate peers to adhere to the protocol and maximise their service contributions to the overlay.

Peer-to-peer networks providing QoS-enabled services are sensitive to hidden action situations, where the actions of a server peer are hidden from the peers who receive services from it. This is because server peers can choose to strategically minimize their effort, and client peers may be unable to distinguish between cases where the server exerted insufficient effort and cases where the server kept its advertised effort levels but the end-to-end conditions in the network were sufficiently adverse. We propose a principal-agent model for hidden action that gives server peers sufficient incentives to meet their advertised effort levels, without client peers having to decide for each transaction whether the outcome was due to server behavior or network conditions. This allows peers to draft contracts that provide incentives for truthful revelation of QoS capabilities, and to have predictable transaction quality. We then exemplify the model for the case of a mesh-based, pull-oriented streaming system with low delay requirements. For this case, we show how to estimate the model parameters a function of the prevailing network conditions, and how to enforce contract fulfillment through a reciprocative strategy.

The distribution of flow sizes is a quantity of interest fundamental to traffic engineering and network modelling and only likely to become more important in the future. The recovery of the flow-length distribution from (sampled) packet data is referred to as flow-inversion. Traditional packet sampling methods cause distortions in a recovered distribution of flow-length. We propose an improved method for inverting data sampled using the technique known as sample-and-hold. We show that the technique improves upon existing inversion techniques illustrated using both real and artificial data sets. The technique described may have applications to other inversion problems.

This paper describes a framework for analysing matches in multiple data sets. The framework described is quite general and can be applied to a variety of problems where matches are to be found in data surveyed at a number of locations (or at a single location over a number of days). As an example, the framework is applied to the problem of false matches in licence plate survey data. The specific problem addressed is that of estimating how many vehicles were genuinely sighted at every one of a number of survey points when there is a possibility of accidentally confusing two vehicles due to the nature of the survey undertaken. In this paper, a method for representing the possible "types of match" is outlined using set theory. The phrase "types of match" will be defined and formalised in this paper. A method for enumerating the set of all types of match over n survey sites, is described. The method is applied to the problem of correcting survey data for false matches using a simple probabalistic method. An algorithm is developed for correcting false matches over multiple survey sites and its use is demonstrated with simulation results., 13 pages, 1 figure