65 results found
Malekzadeh M, Clegg RG, Cavallaro A, et al., Mobile Sensor Data Anonymization, ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI 2019)
Data from motion sensors such as accelerometers and gyroscopes embedded inour devices can reveal secondary undesired, private information about ouractivities. This information can be used for malicious purposes such as useridentification by application developers. To address this problem, we propose adata transformation mechanism that enables a device to share data for specificapplications (e.g.~monitoring their daily activities) without revealing privateuser information (e.g.~ user identity). We formulate this anonymization processbased on an information theoretic approach and propose a new multi-objectiveloss function for training convolutional auto-encoders~(CAEs) to provide apractical approximation to our anonymization problem. This effective lossfunction forces the transformed data to minimize the information about theuser's identity, as well as the data distortion to preserveapplication-specific utility. Our training process regulates the encoder todisregard user-identifiable patterns and tunes the decoder to shape the finaloutput independently of users in the training set. Then, a trained CAE can bedeployed on a user's mobile device to anonymize sensor data before sharing withan app, even for users who are not included in the training dataset. Theresults, on a dataset of 24 users for activity recognition, show a promisingtrade-off on transformed data between utility and privacy, with an accuracy foractivity recognition over 92%, while reducing the chance of identifying a userto less than 7%.
Osia SA, Taheri A, Shamsabadi AS, et 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.
, 2018, Privacy-preserving personal model training, Proceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018, Pages: 153-164
© 2018 IEEE. Many current Internet services rely on inferences from models trained on user data. Commonly, both the training and inference tasks are carried out using cloud resources fed by personal data collected at scale from users. Holding and using such large collections of personal data in the cloud creates privacy risks to the data subjects, but is currently required for users to benefit from such services. We explore how to provide for model training and inference in a system where computation is pushed to the data in preference to moving data to the cloud, obviating many current privacy risks. Specifically, we take an initial model learnt from a small set of users and retrain it locally using data from a single user. We evaluate on two tasks: one supervised learning task, using a neural network to recognise users' current activity from accelerometer traces; and one unsupervised learning task, identifying topics in a large set of documents. In both cases the accuracy is improved. We also analyse the robustness of our approach against adversarial attacks, as well as its feasibility by presenting a performance evaluation on a representative resource-constrained device (a Raspberry Pi).
Osia SA, Shamsabadi AS, Taheri A, et al., 2018, Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning, COMPUTER, Vol: 51, Pages: 42-49, ISSN: 0018-9162
Chamberlain A, Crabtree A, Haddadi H, et al., 2018, Special theme on privacy and the Internet of things, PERSONAL AND UBIQUITOUS COMPUTING, Vol: 22, Pages: 289-292, ISSN: 1617-4909
Crabtree A, Lodge T, Colley J, et al., 2018, Building accountability into the Internet of Things: the IoT Databox model, Journal of Reliable Intelligent Environments, Vol: 4, Pages: 39-55, ISSN: 2199-4668
Malekzadeh M, Clegg RG, Cavallaro A, et al., Protecting Sensory Data against Sensitive Inferences
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.
Hänsel K, Poguntke R, Haddadi H, et 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
Shamsabadi AS, Haddadi H, Cavallaro A, 2018, DISTRIBUTED ONE-CLASS LEARNING, Publisher: IEEE
Perera C, Wakenshaw SYL, Baarslag T, et al., 2017, Valorising the IoT Databox: creating value for everyone, TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, Vol: 28, ISSN: 2161-3915
Crabtree A, Lodge T, Colley J, et al., 2016, Enabling the new economic actor: data protection, the digital economy, and the Databox, PERSONAL AND UBIQUITOUS COMPUTING, Vol: 20, Pages: 947-957, ISSN: 1617-4909
Katevas K, Haddadi H, Tokarchuk L, 2016, SensingKit: Evaluating the Sensor Power Consumption in iOS devices, 12th International Conference on Intelligent Environments (IE), Publisher: IEEE, Pages: 222-225, ISSN: 2469-8792
Rich J, Haddadi H, Hospedales TM, 2016, Towards Bottom-Up Analysis of Social Food, 6th International Conference on Digital Health (DH), Publisher: ASSOC COMPUTING MACHINERY, Pages: 111-120
Amar Y, Haddadi H, Mortier R, 2016, Privacy-Aware Infrastructure for Managing Personal Data Personal Data Arbitering within the Databox Framework, ACM Conference on Special Interest Group on Data Communication (SIGCOMM), Publisher: ASSOC COMPUTING MACHINERY, Pages: 571-572
Tyson G, Perta VC, Haddadi H, et al., 2016, A First Look at User Activity on Tinder, 8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Publisher: IEEE, Pages: 461-466
Cunha TO, Weber I, Haddadi H, et al., 2016, The Effect of Social Feedback in a Reddit Weight Loss Community, 6th International Conference on Digital Health (DH), Publisher: ASSOC COMPUTING MACHINERY, Pages: 99-103
Fard MA, Haddadi H, Targhi AT, 2016, Fruits and Vegetables Calorie Counter Using Convolutional Neural Networks, 6th International Conference on Digital Health (DH), Publisher: ASSOC COMPUTING MACHINERY, Pages: 121-122
Naderi PT, Malazi HT, Ghassemian M, et al., 2016, Quality of Claim Metrics in Social Sensing Systems: A case study on IranDeal, 6th International Conference on Computer and Knowledge Engineering (ICCKE), Publisher: IEEE, Pages: 129-135
Perta VC, Barbera MV, Tyson G, et al., 2015, A Glance through the VPN Looking Glass: IPv6 Leakage and DNS Hijacking in Commercial VPN clients, Pages: 77-91
Falahrastegar M, Haddadi H, Uhlig S, et al., 2014, The Rise of Panopticons: Examining Region-Specific Third-Party Web Tracking, 6th International Workshop on Traffic Monitoring and Analysis (TMA), Publisher: SPRINGER-VERLAG BERLIN, Pages: 104-114, ISSN: 0302-9743
Liu H, Hu Z, Haddadi H, et al., 2013, Hidden link prediction based on node centrality and weak ties, EPL, Vol: 101, ISSN: 0295-5075
Hobbs-Chell H, King AJ, Sharratt H, et al., 2012, Data-loggers carried on a harness do not adversely affect sheep locomotion, RESEARCH IN VETERINARY SCIENCE, Vol: 93, Pages: 549-552, ISSN: 0034-5288
Cha M, Benevenuto F, Haddadi H, et al., 2012, The World of Connections and Information Flow in Twitter, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, Vol: 42, Pages: 991-998, ISSN: 1083-4427
Vallina-Rodriguez N, Scellato S, Haddadi H, et al., 2012, Los Twindignados: The Rise of the Indignados Movement on Twitter, ASE/IEEE International Conference on Privacy, Security, Risk and Trust / ASE/IEEE International Confernece on Social Computing (SocialCom/PASSAT), Publisher: IEEE, Pages: 496-501
Fay D, Haddadi H, Uhlig S, et al., 2011, Discriminating graphs through spectral projections, COMPUTER NETWORKS, Vol: 55, Pages: 3458-3468, ISSN: 1389-1286
Haddadi H, King AJ, Wills AP, et al., 2011, Determining association networks in social animals: choosing spatial-temporal criteria and sampling rates, BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY, Vol: 65, Pages: 1659-1668, ISSN: 0340-5443
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