Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. ...
详细信息
Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. ...
ISBN:
(纸本)9781450342636
Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. If the rapid proliferation of networks and high-tech miniature sensors makes this reality possible, the ever-growing complexity of the metrics and parameters governing such systems raises serious issues in terms of privacy, security and computing capability. In this demonstration, we show a new system, able to estimate a user's mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a user's profile generated locally in the form of a spider graph. In particular, this application uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information. A video showing the different interfaces to be presented is available online.
the increased popularity of mobile devices widens opportunities for a user either to lose the device or to have the device stolen and compromised. At the same time, user interaction with a mobile device generates a un...
详细信息
ISBN:
(纸本)9781450322782
the increased popularity of mobile devices widens opportunities for a user either to lose the device or to have the device stolen and compromised. At the same time, user interaction with a mobile device generates a unique set of features such as dialed numbers, timestamps of communication activities, contacted base stations, etc. this work proposes several methods to identify the user based on her communications history. Specifically, the proposed methods detect an abnormality based on the behavior fingerprint generated by a set of features from the network for each user session. We present an implementation of such methods that use features from real SMS, and voice call records from a major tier 1 cellular operator. this can potentially trigger a rapid reaction upon an unauthorized user gaining control of a lost or stolen terminal, preventing data compromise and device misuse. the proposed solution can also detect background malicious traffic originated by, for example, a malicious application running on the mobile device. Our experiments with annonymized data from 10,000 users, representing over 14 million SMS and voice call detail records, show that the proposed methods are scalable and can continuously identify millions of mobile users while preserving dataprivacy, and achieving low false positives and high misuse detection rates with low storage and computation overhead. Copyright 2014acm.
We present an efficient protocol for privacy-preserving evaluation of diagnostic programs, represented as binary decision trees or branching programs. the protocol applies a branching diagnostic program with classific...
详细信息
ISBN:
(纸本)9781595937032
We present an efficient protocol for privacy-preserving evaluation of diagnostic programs, represented as binary decision trees or branching programs. the protocol applies a branching diagnostic program with classification labels in the leaves to the user's attribute vector. the user learns only the label assigned by the program to his vector;the diagnostic program itself remains secret. the program's owner does not learn anything. Our construction is significantly more efficient than those obtained by direct application of generic secure multi-party computation techniques. We use our protocol to implement a privacy-preserving version of the Clarify system for software fault diagnosis, and demonstrate that its performance is acceptable for many practical scenarios.
暂无评论