Faculty of EngineeringDyson School of Design Engineering

Senior Lecturer

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### Location

Dyson BuildingSouth Kensington Campus

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## Publications

Publication Type
Year
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64 results found

Clegg RG, Withall MS, Moore AW, Phillips IW, Parish DJ, Rio M, Landa R, Haddadi H, Kyriakopoulos K, Auge J, Clayton R, Salmon Det al., 2009, Challenges in the capture and dissemination of measurements from high-speed networks, IET COMMUNICATIONS, Vol: 3, Pages: 957-966, ISSN: 1751-8628

JOURNAL ARTICLE

Haddadi H, Fay D, Jamakovic A, Maennel O, Moore AW, Mortier R, Uhlig Set al., 2009, On the Importance of Local Connectivity for Internet Topology Models, 21st International Teletraffic Congress (ITC 21), Publisher: IEEE, Pages: 177-184

CONFERENCE PAPER

CONFERENCE PAPER

Haddadi H, Fay D, Uhlig S, Moore A, Mortier R, Jamakovic A, Rio Met al., 2008, Tuning topology generators using spectral distributions, SPEC International Performance Evaluation Workshop (SIPEW 2008), Publisher: SPRINGER-VERLAG BERLIN, Pages: 154-+, ISSN: 0302-9743

CONFERENCE PAPER

Haddadi H, Landa R, Moore AW, Bhatti S, Rio M, Che Xet al., 2008, Revisiting the Issues On Netflow Sample and Export Performance, 3rd International Conference on Communications and Networking in China, Publisher: IEEE, Pages: 421-+

CONFERENCE PAPER

Haddadi H, Rio M, Iannaccone G, Moore A, Mortier Ret al., 2008, NETWORK TOPOLOGIES: INFERENCE, MODELING, AND GENERATION, IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, Vol: 10, Pages: 49-70, ISSN: 1553-877X

JOURNAL ARTICLE

Haddadi H, Uhlig S, Moore A, Mortier R, Rio Met al., 2008, Modeling Internet topology dynamics, ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, Vol: 38, Pages: 65-68, ISSN: 0146-4833

JOURNAL ARTICLE

Britton M, Shum V, Sacks L, Haddadi Het al., 2005, A biologically-inspired approach to designing wireless sensor networks, 2nd European Workshop on Wireless Sensor Networks, Publisher: IEEE, Pages: 256-266

CONFERENCE PAPER

Amar Y, Haddadi H, Mortier R, Brown A, Colley J, Crabtree Aet al., An Analysis of Home IoT Network Traffic and Behaviour

Internet-connected devices are increasingly present in our homes, and privacybreaches, data thefts, and security threats are becoming commonplace. In orderto avoid these, we must first understand the behaviour of these devices. In this work, we analyse network traces from a testbed of common IoT devices,and describe general methods for fingerprinting their behavior. We then use theinformation and insights derived from this data to assess where privacy andsecurity risks manifest themselves, as well as how device behavior affectsbandwidth. We demonstrate simple measures that circumvent attempts at securingdevices and protecting privacy.

JOURNAL ARTICLE

Clegg RG, Haddadi H, Landa R, Rio Met al., Towards Informative Statistical Flow Inversion

A problem which has recently attracted research attention is that ofestimating the distribution of flow sizes in internet traffic. On high trafficlinks it is sometimes impossible to record every packet. Researchers haveapproached the problem of estimating flow lengths from sampled packet data intwo separate ways. Firstly, different sampling methodologies can be tried tomore accurately measure the desired system parameters. One such method is thesample-and-hold method where, if a packet is sampled, all subsequent packets inthat flow are sampled. Secondly, statistical methods can be used to invert''the sampled data and produce an estimate of flow lengths from a sample. In this paper we propose, implement and test two variants on thesample-and-hold method. In addition we show how the sample-and-hold method canbe inverted to get an estimation of the genuine distribution of flow sizes.Experiments are carried out on real network traces to compare standard packetsampling with three variants of sample-and-hold. The methods are compared fortheir ability to reconstruct the genuine distribution of flow sizes in thetraffic.

JOURNAL ARTICLE

Social media have substantially altered the way brands and businessesadvertise: Online Social Networks provide brands with more versatile anddynamic channels for advertisement than traditional media (e.g., TV and radio).Levels of engagement in such media are usually measured in terms of contentadoption (e.g., likes and retweets) and sentiment, around a given topic.However, sentiment analysis and topic identification are both non-trivialtasks. In this paper, using data collected from Twitter as a case study, we analyzehow engagement and sentiment in promoted content spread over a 10-day period.We find that promoted tweets lead to higher positive sentiment than promotedtrends; although promoted trends pay off in response volume. We observe thatlevels of engagement for the brand and promoted content are highest on thefirst day of the campaign, and fall considerably thereafter. However, we showthat these insights depend on the use of robust machine learning and naturallanguage processing techniques to gather focused, relevant datasets, and toaccurately gauge sentiment, rather than relying on the simple keyword- orfrequency-based metrics sometimes used in social media research.

JOURNAL ARTICLE

Falahrastegar M, Haddadi H, Uhlig S, Mortier Ret al., Anatomy of the Third-Party Web Tracking Ecosystem

The presence of third-party tracking on websites has become customary.However, our understanding of the third-party ecosystem is still veryrudimentary. We examine third-party trackers from a geographical perspective,observing the third-party tracking ecosystem from 29 countries across theglobe. When examining the data by region (North America, South America, Europe,East Asia, Middle East, and Oceania), we observe significant geographicalvariation between regions and countries within regions. We find trackers thatfocus on specific regions and countries, and some that are hosted in countriesoutside their expected target tracking domain. Given the differences inregulatory regimes between jurisdictions, we believe this analysis sheds lighton the geographical properties of this ecosystem and on the problems that thesemay pose to our ability to track and manage the different data silos that nowstore personal data about us all.

JOURNAL ARTICLE

Fay D, Haddadi H, Seto MC, Wang H, Kling CCet al., An exploration of fetish social networks and communities

Online Social Networks (OSNs) provide a venue for virtual interactions andrelationships between individuals. In some communities, OSNs also facilitatearranging online meetings and relationships. FetLife, the worlds largestanonymous social network for the BDSM, fetish and kink communities, provides aunique example of an OSN that serves as an interaction space, communityorganizing tool, and sexual market. In this paper, we present a first look atthe characteristics of European members of Fetlife, comprising 504,416individual nodes with 1,912,196 connections. We looked at user characteristicsin terms of gender, sexual orientation, and preferred role. We further examinedthe topological and structural properties of groups, as well as the type ofinteractions and relations between their members. Our results suggest there areimportant differences between the FetLife community and conventional OSNs. Thenetwork can be characterised by complex gender based interactions both from asexual market and platonic viewpoint which point to a truly fascinating socialnetwork.

JOURNAL ARTICLE

Hänsel K, Poguntke R, Haddadi H, Alomainy A, Schmidt Aet al., What to Put on the User: Sensing Technologies for Studies and Physiology Aware Systems, ACM Conference on Human Factors in Computing Systems (ACM CHI’18), Publisher: ACM

CONFERENCE PAPER

Formation of recirculating wakes is a prominent feature of inertial flowaround bluff bodies. Below the onset of vortex shedding in uniform unboundedflows, the fluid in the recirculating wake region moves on closed planarorbits. The steady wake is thus an isolated zone in the flow and does notexchange fluid with the free stream. In this work, we utilize lattice-Boltzmannsimulations and microfluidic experiments to demonstrate that in microchannelinertial flow of Newtonian fluids, the recirculating wake is replaced by athree-dimensional vortical flow. Spiraling streamlines generate a continuousexchange of fluid between the vortex behind the obstacle and the free stream.The flow inertia is represented by Reynolds number defined as $Re =\frac{u_{max}D_{y}}{\nu}$, where $u_{max}$ is the maximum fluid velocity in thechannel inlet, $D_{y}$ is the characteristic obstacle length and $\nu$ is thefluid kinematic viscosity. We discuss the effects of $Re$, the obstacle shapeand the wall confinement on the fluid entry into the vortex. Further, wedemonstrate that in flow of a dilute suspension of particles around theobstacle, the fluid entry into the vortex can result in entrapment of particlesas well.

JOURNAL ARTICLE

Haddadi H, Fay D, Jamakovic A, Maennel O, Moore AW, Mortier R, Rio M, Uhlig Set al., Beyond Node Degree: Evaluating AS Topology Models

Many models have been proposed to generate Internet Autonomous System (AS)topologies, most of which make structural assumptions about the AS graph. Inthis paper we compare AS topology generation models with several observed AStopologies. In contrast to most previous works, we avoid making assumptionsabout which topological properties are important to characterize the AStopology. Our analysis shows that, although matching degree-based properties,the existing AS topology generation models fail to capture the complexity ofthe local interconnection structure between ASs. Furthermore, we use BGP datafrom multiple vantage points to show that additional measurement locationssignificantly affect local structure properties, such as clustering and nodecentrality. Degree-based properties, however, are not notably affected byadditional measurements locations. These observations are particularly valid inthe core. The shortcomings of AS topology generation models stems from anunderestimation of the complexity of the connectivity in the core caused byinappropriate use of BGP data.

JOURNAL ARTICLE

Haddadi H, Howard H, Chaudhry A, Crowcroft J, Madhavapeddy A, Mortier Ret al., Personal Data: Thinking Inside the Box

We propose there is a need for a technical platform enabling people to engagewith the collection, management and consumption of personal data; and that thisplatform should itself be personal, under the direct control of the individualwhose data it holds. In what follows, we refer to this platform as the Databox,a personal, networked service that collates personal data and can be used tomake those data available. While your Databox is likely to be a virtualplatform, in that it will involve multiple devices and services, at least oneinstance of it will exist in physical form such as on a physical form-factorcomputing device with associated storage and networking, such as a home hub.

JOURNAL ARTICLE

Haddadi H, Ofli F, Mejova Y, Weber I, Srivastava Jet al., 360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise ofthe Quantified Self movement, where individuals log everything ranging from thenumber of steps they have taken, to their heart rate, to their sleepingpatterns. Sensors do not, however, typically sense the social and ambientenvironment of the users, such as general life style attributes or informationabout their social network. This means that the users themselves, and themedical practitioners, privy to the wearable sensor data, only have a narrowview of the individual, limited mainly to certain aspects of their physicalcondition. In this paper we describe a number of use cases for how social media can beused to complement the check-up data and those from sensors to gain a moreholistic view on individuals' health, a perspective we call the 360 QuantifiedSelf. Health-related information can be obtained from sources as diverse asfood photo sharing, location check-ins, or profile pictures. Additionally,information from a person's ego network can shed light on the social dimensionof wellbeing which is widely acknowledged to be of utmost importance, eventhough they are currently rarely used for medical diagnosis. We articulate along-term vision describing the desirable list of technical advances andvariety of data to achieve an integrated system encompassing Electronic HealthRecords (EHR), data from wearable devices, alongside information derived fromsocial media data.

JOURNAL ARTICLE

Haddadi H, Smaragdakis G, Ramakrishnan KK, Opportunities in a Federated Cloud Marketplace

Recent measurement studies show that there are massively distributed hostingand computing infrastructures deployed in the Internet. Such infrastructuresinclude large data centers and organizations' computing clusters. When idle,these resources can readily serve local users. Such users can be smartphone ortablet users wishing to access services such as remote desktop or CPU/bandwidthintensive activities. Particularly, when they are likely to have high latencyto access, or may have no access at all to, centralized cloud providers. Today,however, there is no global marketplace where sellers and buyers of availableresources can trade. The recently introduced marketplaces of Amazon and othercloud infrastructures are limited by the network footprint of their owninfrastructures and availability of such services in the target country andregion. In this article we discuss the potentials for a federated cloudmarketplace where sellers and buyers of a number of resources, includingstorage, computing, and network bandwidth, can freely trade. This ecosystem canbe regulated through brokers who act as service level monitors and auctioneers.We conclude by discussing the challenges and opportunities in this space.

JOURNAL ARTICLE

Haris M, Haddadi H, Hui P, Privacy Leakage in Mobile Computing: Tools, Methods, and Characteristics

The number of smartphones, tablets, sensors, and connected wearable devicesare rapidly increasing. Today, in many parts of the globe, the penetration ofmobile computers has overtaken the number of traditional personal computers.This trend and the always-on nature of these devices have resulted inincreasing concerns over the intrusive nature of these devices and the privacyrisks that they impose on users or those associated with them. In this paper,we survey the current state of the art on mobile computing research, focusingon privacy risks and data leakage effects. We then discuss a number of methods,recommendations, and ongoing research in limiting the privacy leakages andassociated risks by mobile computing.

JOURNAL ARTICLE

Hänsel K, Wilde N, Haddadi H, Alomainy Aet al., Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making iteasier for an individual to monitor their behaviours. Previously self generateddata interacts with the user to motivate positive behaviour change, but issuesarise when relating this to long term mention of wearable devices. Previousstudies within this area are discussed. We also consider a new approach wheredata is used to support instead of motivate, through monitoring and logging toencourage reflection. Based on issues highlighted, we then make recommendationson the direction in which future work could be most beneficial.

JOURNAL ARTICLE

There is growing concern about how personal data are used when users grantapplications direct access to the sensors of their mobile devices. In fact,high resolution temporal data generated by motion sensors reflect directly theactivities of a user and indirectly physical and demographic attributes. Inthis paper, we propose a feature learning architecture for mobile devices thatprovides flexible and negotiable privacy-preserving sensor data transmission byappropriately transforming raw sensor data. The objective is to move from thecurrent binary setting of granting or not permission to an application, towarda model that allows users to grant each application permission over a limitedrange of inferences according to the provided services. The internal structureof each component of the proposed architecture can be flexibly changed and thetrade-off between privacy and utility can be negotiated between the constraintsof the user and the underlying application. We validated the proposedarchitecture in an activity recognition application using two real-worlddatasets, with the objective of recognizing an activity without disclosinggender as an example of private information. Results show that the proposedframework maintains the usefulness of the transformed data for activityrecognition, with an average loss of only around three percentage points, whilereducing the possibility of gender classification to around 50\%, the targetrandom guess, from more than 90\% when using raw sensor data. We also presentand distribute MotionSense, a new dataset for activity and attributerecognition collected from motion sensors.

CONFERENCE PAPER

An increasing number of sensors on mobile, Internet of things (IoT), andwearable devices generate time-series measurements of physical activities.Though access to the sensory data is critical to the success of many beneficialapplications such as health monitoring or activity recognition, a wide range ofpotentially sensitive information about the individuals can also be discoveredthrough access to sensory data and this cannot easily be protected usingtraditional privacy approaches. In this paper, we propose a privacy-preserving sensing framework for managingaccess to time-series data in order to provide utility while protectingindividuals' privacy. We introduce Replacement AutoEncoder, a novel algorithmwhich learns how to transform discriminative features of data that correspondto sensitive inferences, into some features that have been more observed innon-sensitive inferences, to protect users' privacy. This efficiency isachieved by defining a user-customized objective function for deepautoencoders. Our replacement method will not only eliminate the possibility ofrecognizing sensitive inferences, it also eliminates the possibility ofdetecting the occurrence of them. That is the main weakness of other approachessuch as filtering or randomization. We evaluate the efficacy of the algorithmwith an activity recognition task in a multi-sensing environment usingextensive experiments on three benchmark datasets. We show that it can retainthe recognition accuracy of state-of-the-art techniques while simultaneouslypreserving the privacy of sensitive information. Finally, we utilize the GANsfor detecting the occurrence of replacement, after releasing data, and showthat this can be done only if the adversarial network is trained on the users'original data.

JOURNAL ARTICLE

Mejova Y, Abbar S, Haddadi H, Fetishizing Food in Digital Age: #foodporn Around the World

What food is so good as to be considered pornographic? Worldwide, the popular#foodporn hashtag has been used to share appetizing pictures of peoples'favorite culinary experiences. But social scientists ask whether #foodpornpromotes an unhealthy relationship with food, as pornography would contributeto an unrealistic view of sexuality. In this study, we examine nearly 10million Instagram posts by 1.7 million users worldwide. An overwhelming (anduniform across the nations) obsession with chocolate and cake shows thedomination of sugary dessert over local cuisines. Yet, we find encouragingtraits in the association of emotion and health-related topics with #foodporn,suggesting food can serve as motivation for a healthy lifestyle. Socialapproval also favors the healthy posts, with users posting with healthyhashtags having an average of 1,000 more followers than those with unhealthyones. Finally, we perform a demographic analysis which shows nation-wide trendsof behavior, such as a strong relationship (r=0.51) between the GDP per capitaand the attention to healthiness of their favorite food. Our results expose anew facet of food "pornography", revealing potential avenues for utilizing thisprecarious notion for promoting healthy lifestyles.

JOURNAL ARTICLE

Mejova Y, Haddadi H, Noulas A, Weber Iet al., #FoodPorn: Obesity Patterns in Culinary Interactions

We present a large-scale analysis of Instagram pictures taken at 164,753restaurants by millions of users. Motivated by the obesity epidemic in theUnited States, our aim is three-fold: (i) to assess the relationship betweenfast food and chain restaurants and obesity, (ii) to better understand people'sthoughts on and perceptions of their daily dining experiences, and (iii) toreveal the nature of social reinforcement and approval in the context ofdietary health on social media. When we correlate the prominence of fast foodrestaurants in US counties with obesity, we find the Foursquare data to show agreater correlation at 0.424 than official survey data from the County HealthRankings would show. Our analysis further reveals a relationship between smallbusinesses and local foods with better dietary health, with such restaurantsgetting more attention in areas of lower obesity. However, even in such areas,social approval favors the unhealthy foods high in sugar, with donut shopsproducing the most liked photos. Thus, the dietary landscape our study revealsis a complex ecosystem, with fast food playing a role alongside socialinteractions and personal perceptions, which often may be at odds.

JOURNAL ARTICLE

Mortier R, Haddadi H, Henderson T, McAuley D, Crowcroft Jet al., Human-Data Interaction: The Human Face of the Data-Driven Society

The increasing generation and collection of personal data has created acomplex ecosystem, often collaborative but sometimes combative, aroundcompanies and individuals engaging in the use of these data. We propose thatthe interactions between these agents warrants a new topic of study: Human-DataInteraction (HDI). In this paper we discuss how HDI sits at the intersection ofvarious disciplines, including computer science, statistics, sociology,psychology and behavioural economics. We expose the challenges that HDI raises,organised into three core themes of legibility, agency and negotiability, andwe present the HDI agenda to open up a dialogue amongst interested parties inthe personal and big data ecosystems.

JOURNAL ARTICLE

Nithyanand R, Khattak S, Javed M, Vallina-Rodriguez N, Falahrastegar M, Powles JE, Cristofaro ED, Haddadi H, Murdoch SJet al., Ad-Blocking and Counter Blocking: A Slice of the Arms Race

Adblocking tools like Adblock Plus continue to rise in popularity,potentially threatening the dynamics of advertising revenue streams. Inresponse, a number of publishers have ramped up efforts to develop and deploymechanisms for detecting and/or counter-blocking adblockers (which we refer toas anti-adblockers), effectively escalating the online advertising arms race.In this paper, we develop a scalable approach for identifying third-partyservices shared across multiple web-sites and use it to provide a firstcharacterization of anti-adblocking across the Alexa Top-5K websites. We mapwebsites that perform anti-adblocking as well as the entities that provideanti-adblocking scripts. We study the modus operandi of these scripts and theirimpact on popular adblockers. We find that at least 6.7% of websites in theAlexa Top-5K use anti-adblocking scripts, acquired from 12 distinct entities --some of which have a direct interest in nourishing the online advertisingindustry.

JOURNAL ARTICLE

Osia SA, Shamsabadi AS, Taheri A, Katevas K, Rabiee HR, Lane ND, Haddadi Het al., Privacy-Preserving Deep Inference for Rich User Data on The Cloud

Deep neural networks are increasingly being used in a variety of machinelearning applications applied to rich user data on the cloud. However, thisapproach introduces a number of privacy and efficiency challenges, as the cloudoperator can perform secondary inferences on the available data. Recently,advances in edge processing have paved the way for more efficient, and private,data processing at the source for simple tasks and lighter models, though theyremain a challenge for larger, and more complicated models. In this paper, wepresent a hybrid approach for breaking down large, complex deep models forcooperative, privacy-preserving analytics. We do this by breaking down thepopular deep architectures and fine-tune them in a particular way. We thenevaluate the privacy benefits of this approach based on the information exposedto the cloud service. We also asses the local inference cost of differentlayers on a modern handset for mobile applications. Our evaluations show thatby using certain kind of fine-tuning and embedding techniques and at a smallprocessing costs, we can greatly reduce the level of information available tounintended tasks applied to the data feature on the cloud, and hence achievingthe desired tradeoff between privacy and performance.

JOURNAL ARTICLE

Osia SA, Shamsabadi AS, Taheri A, Katevas K, Sajadmanesh S, Rabiee HR, Lane ND, Haddadi Het al., A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

Deep Neural Networks are increasingly being used in a variety of machinelearning applications applied to user data on the cloud. However, this approachintroduces a number of privacy and efficiency challenges, as the cloud operatorcan perform secondary inferences on the available data. Recently, advances inedge processing have paved the way for more efficient, and private, dataprocessing at the source for simple tasks and lighter models, though theyremain a challenge for larger, and more complicated models. In this paper, wepresent a hybrid approach for breaking down large, complex deep models forcooperative, privacy-preserving analytics. We do this by breaking down thepopular deep architectures and fine-tune them in a suitable way. We thenevaluate the privacy benefits of this approach based on the information exposedto the cloud service. We also assess the local inference cost of differentlayers on a modern handset for mobile applications. Our evaluations show thatby using certain kind of fine-tuning and embedding techniques and at a smallprocessing cost, we can greatly reduce the level of information available tounintended tasks applied to the data features on the cloud, and hence achievingthe desired tradeoff between privacy and performance.

JOURNAL ARTICLE

Osia SA, Taheri A, Shamsabadi AS, Katevas K, Haddadi H, Rabiee HRet al., Deep Private-Feature Extraction, IEEE Transactions on Knowledge and Data Engineering, ISSN: 1041-4347

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep modelwhich is trained and evaluated based on information theoretic constraints.Using the selective exchange of information between a user's device and aservice provider, DPFE enables the user to prevent certain sensitiveinformation from being shared with a service provider, while allowing them toextract approved information using their model. We introduce and utilize thelog-rank privacy, a novel measure to assess the effectiveness of DPFE inremoving sensitive information and compare different models based on theiraccuracy-privacy tradeoff. We then implement and evaluate the performance ofDPFE on smartphones to understand its complexity, resource demands, andefficiency tradeoffs. Our results on benchmark image datasets demonstrate thatunder moderate resource utilization, DPFE can achieve high accuracy for primarytasks while preserving the privacy of sensitive features.

JOURNAL ARTICLE

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