 |
International Conferences: Mobile Ad Hoc Networking and Computing 2015 China |
The ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc ’15) addressed wireless networking and computing. It included the Workshop on Privacy-Aware Mobile Computing (PAMCO) and was held at Hangzhou, China, June 22–25, 2015. Topics offered included foundations for privacy-aware mobile computing—e.g., key exchange, distribution and management, location privacy, privacy-preserving data collection, privacy-preserving data aggregation and analytics, privacy issues in wearable computing, data analysis on traffic logs, privacy issues in cellular networks, privacy issues in body-area networks, emerging privacy threats from mobile apps, privacy issues in near-field communication (NFC), Bluetooth security and privacy, secure and privacy-preserving cooperation, jamming and counter measures, and capacity and security analysis of covert channels.
Qinggang Yue, Zhen Ling, Wei Yu, Benyuan Liu, Xinwen Fu; “Blind Recognition of Text Input on Mobile Devices via Natural Language Processing,” PAMCO ’15 Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing, June 2015, Pages 19-24. doi:10.1145/2757302.2757304
Abstract: In this paper, we investigate how to retrieve meaningful English text input on mobile devices from recorded videos while the text is illegible in the videos. In our previous work, we were able to retrieve random passwords with high success rate at a certain distance. When the distance increases, the success rate of recovering passwords decreases. However, if the input is meaningful text such as email messages, we can further increase the success rate via natural language processing techniques since the text follows spelling and grammar rules and is context sensitive. The process of retrieving the text from videos can be modeled as noisy channels. We first derive candidate words for each word of the input sentence, model the whole sentence with a Hidden Markov model and then apply the trigram language model to derive the original sentence. Our experiments validate our technique of retrieving meaningful English text input on mobile devices from recorded videos.
Keywords: computer vision, mobile security, natural language processing (ID#: 15-6858)
URL: http://doi.acm.org/10.1145/2757302.2757304
Maya Larson, Chunqiang Hu, Ruinian Li, Wei Li, Xiuzhen Cheng; “Secure Auctions without an Auctioneer via Verifiable Secret Sharing,” in PAMCO ’15 Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing, June 2015, Pages 1-6. doi:10.1145/2757302.2757305
Abstract: Combinatorial auctions are a research hot spot. They impact people’s daily lives in many applications such as spectrum auctions held by the FCC. In such auctions, bidders may want to submit bids for combinations of goods. The challenge is how to protect the privacy of bidding prices and ensure data security in these auctions? To tackle this challenge, we present an approach based on verifiable secret sharing. The approach is to represent the price in the degree of a polynomial; thus the maximum/sum of the degree of two polynomials can be obtained by the degree of the sum/product of the two polynomials based on secret sharing. This protocol hides the information of bidders (bidding price) from the auction servers. The auctioneers can obtain their secret shares from bidders without a secure channel. Since it doesn’t need a secure channel, this scheme is more practical and applicable to more scenarios. This scheme provides resistance to collusion attacks, conspiracy attacks, passive attacks and so on. Compared to [11, 12], our proposed scheme provides authentication without increasing the communication cost.
Keywords: (not provided) (ID#: 15-6859)
URL: http://doi.acm.org/10.1145/2757302.2757305
Tong Yan, Yachao Lu, Nan Zhang; “Privacy Disclosure from Wearable Devices,” in PAMCO ’15 Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing, June 2015, Pages 13–18. doi:10.1145/2757302.2757306
Abstract: In recent years, wearable devices have seen an explosive growth of popularity and a rapid enhancement of functionalities. Current off-the-shelf wearable devices offer pack sensors such as pedometer, gyroscope, accelerometer, altimeter, compass, GPS, and heart rate monitor. These sensors work together to quietly monitor various aspects of a user’s daily life, enabling a wide spectrum of health- and social-related applications. Nevertheless, the data collected by such sensors, even in their aggregated form, may cause significant privacy concerns if shared with third-party applications and/or a user’s social connections (as many wearable platforms now support). This paper studies a novel problem of the potential inference of sensitive user behavior from seemingly insensitive sensor outputs. Specifically, we examine whether it is possible to infer the behavioral sequence of a user such as moving from one place to another, visiting a coffee shop, grocery shopping, etc., based on the outputs of pedometer sensors (aggregated over certain time intervals, e.g., 1 minute). We demonstrate through real-world experiments that it is often possible to infer such behavior with a high success probability, raising privacy concerns on the sharing of such information as currently supported by various wearable devices.
Keywords: data mining, information retrieval, privacy, time series, wearable devices (ID#: 15-6860)
URL: http://doi.acm.org/10.1145/2757302.2757306
Zhongli Liu, Zupei Li, Benyuan Liu, Xinwen Fu, Ioannis Raptis, Kui Ren; “Rise of Mini-Drones: Applications and Issues,” in PAMCO ’15 Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing, June 2015, Pages 7–12. doi:10.1145/2757302.2757303
Abstract: Miniature (mini) drones are enjoying increasing attention. They have a broad market and applications. However, a powerful technology often has two ethical sides. Miniature drones can be abused, rendering security and privacy concerns. The contribution of this paper is two-fold. First, we will perform a survey of mini-drones on market and compare their specifications such as flight time, maximum payload weight, and price, and regulations and issues of operating mini-drones. Second, we propose novel aerial localization strategies and compare six different localization strategies for a thorough study of aerial localization by a single drone.
Keywords: (not provided) (ID#: 15-6861)
URL: http://doi.acm.org/10.1145/2757302.2757303
Xinwen Fu, Nan Zhang, Program Chairs; “Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing,” PAMCO ’15 at MobiHoc ’15, Hangzhou, China, June 22–25, 2015, ACM, New York, NY, 2015. ISBN: 978-1-4503-3523-2
Abstract: It is our great pleasure to welcome you to the 2015 ACM MobiHoc Workshop on Privacy-Aware Mobile Computing–PAMCO’15. This is the first year of this workshop, which aims to bring together researchers from mobile computing and security/privacy communities to discuss topics related to the protection of privacy in mobile computing, including both theoretical studies and implementation/experimentations papers, especially analysis of privacy threats from emerging applications in mobile environments — e.g., location-based services, mobile apps, wearable computing, etc.
Keywords: (not provided) (ID#: 15-6862)
URL: http://dl.acm.org/citation.cfm?id=2757302&coll=DL&dl=GUIDE&CFID=713685223&CFTOKEN=18305797
Shanhe Yi, Cheng Li, Qun Li; “A Survey of Fog Computing: Concepts, Applications and Issues,” in Mobidata ’15 Proceedings of the 2015 Workshop on Mobile Big Data, June 2015, Pages 37–42. doi:10.1145/2757384.2757397
Abstract: Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.
Keywords: cloud computing, edge computing, fog computing, mobile cloud computing, mobile edge computing, review
(ID#: 15-6863)
URL: http://doi.acm.org/10.1145/2757384.2757397
Jian Liu, Yan Wang, Yingying Chen, Jie Yang, Xu Chen, Jerry Cheng; “Tracking Vital Signs During Sleep Leveraging
Off-the-Shelf WiFi,” in Mobidata ’15 Proceedings of the 2015 Workshop on Mobile Big Data, June 2015, Pages 267–276. doi:10.1145/2746285.2746303
Abstract: Tracking human vital signs of breathing and heart rates during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., Polysomnography (PSG)) are limited to clinic usage. Recent radio frequency (RF) based approaches require specialized devices or dedicated wireless sensors and are only able to track breathing rate. In this work, we propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system re-uses existing WiFi network and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. The developed algorithm makes use of the channel information in both time and frequency domain to estimate breathing and heart rates, and it works well when either individual or two persons are in bed. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing non-invasive, continuous fine-grained vital signs monitoring without any additional cost.
Keywords: channel state information (csi), sleep monitoring, vital signs, wifi (ID#: 15-6864)
URL: http://doi.acm.org/10.1145/2746285.2746303
Zhongli Liu, Zupei Li, Benyuan Liu, Xinwen Fu, Ioannis Raptis, Kui Ren; “Rise of Mini-Drones: Applications and Issues,” in PAMCO ’15 Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing, June 2015, Pages 7–12. doi:10.1145/2757302.2757303
Abstract: Miniature (mini) drones are enjoying increasing attention. They have a broad market and applications. However, a powerful technology often has two ethical sides. Miniature drones can be abused, rendering security and privacy concerns. The contribution of this paper is two-fold. First, we will perform a survey of mini-drones on market and compare their specifications such as flight time, maximum payload weight, and price, and regulations and issues of operating mini-drones. Second, we propose novel aerial localization strategies and compare six different localization strategies for a thorough study of aerial localization by a single drone.
Keywords: (not provided) (ID#: 15-6865)
URL: http://doi.acm.org/10.1145/2757302.2757303
Yu Cao, Peng Hou, Donald Brown, Jie Wang, Songqing Chen; “Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing,” in Mobidata ’15 Proceedings of the 2015 Workshop on Mobile Big Data, June 2015, Pages 43–48. doi:10.1145/2757384.2757398
Abstract: Biomedical research and clinical practice are entering a data-driven era. One of the major applications of biomedical big data research is to utilize inexpensive and unobtrusive mobile biomedical sensors and cloud computing for pervasive health monitoring. However, real-world user experiences with mobile cloud-based health monitoring were poor, due to the factors such as excessive networking latency and longer response time. On the other hand, fog computing, a newly proposed computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, storage, communication, and etc. This new computing paradigm, if successfully applied for pervasive health monitoring, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. In this paper, we employ a real-world pervasive health monitoring application (pervasive fall detection for stroke mitigation) to demonstrate the effectiveness and efficacy of fog computing paradigm in health monitoring. Fall is a major source of morbidity and mortality among stroke patients. Hence, detecting falls automatically and in a timely manner becomes crucial for stroke mitigation in daily life. In this paper, we set to (1) investigate and develop new fall detection algorithms and (2) design and employ a real-time fall detection system employing fog computing paradigm (e.g., distributed analytics and edge intelligence), which split the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud). Experimental results show that distributed analytics and edge intelligence, supported by fog computing paradigm, are very promising solutions for pervasive health monitoring.
Keywords: distributed analytics, edge intelligence, fog computing, mobile computing, pervasive health monitoring (ID#: 15-6866)
URL: http://doi.acm.org/10.1145/2757384.2757398
Jiajia Liu, Nei Kato; “Device-to-Device Communication Overlaying Two-Hop Multi-Channel Uplink Cellular Networks,” in MobiHoc ’15 Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, June 2015, Pages 307–316. doi:10.1145/2746285.2746311
Abstract: Different from previous works, in this paper, we adopt D2D communication as a routing extension to traditional cellular uplinks thus enabling a two-hop route between a user and the serving BS via a D2D relay. Specifically, a BS establishes a cellular link with a mobile user only if the pilot signal strength received from the user is above a specified threshold; otherwise, the user may establish a D2D link with a neighboring user and connect to a nearby BS in a two-hop manner. We present a stochastic geometry based framework to analyze the coverage probability and average rate in such a two-hop multi-channel uplink cellular network where mobile users adopt the fractional channel inversion power control with maximum transmit power limit. As validated by extensive numerical results, the developed framework enables network designers to efficiently determine the optimal control parameters so as to achieve the optimum system performance. Our results show that employing D2D link based two-hop connection can significantly improve both the network coverage and average rate for uplink traffic.
Keywords: device-to-device communication, fractional power control, multi-channel cellular network, stochastic geometry, uplink (ID#: 15-6867)
URL: http://doi.acm.org/10.1145/2746285.2746311
Xi Xiong, Zheng Yang, Longfei Shangguan, Yun Fei, Milos Stojmenovic, Yunhao Liu; “SmartGuide: Towards Single-Image Building Localization with Smartphone,” in MobiHoc ’15 Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, June 2015, Pages 117–126. doi:10.1145/2746285.2746294
Abstract: We introduce SmartGuide, a light-weighted and efficient approach to localize and recognize a distant unknown building. Our approach relies on shooting only a single photo of a target building via a smartphone and a local 2D Google map. SmartGuide first extracts a partial top view contour of a building from its side-view photo by applying vanishing point and the Manhattan World Assumption, and then fetches a candidate building set from a local 2D Google map based on smartphone’s GPS readings. Partial top view shape, orientation and distance relative to the camera are used as input parameters in a probability model, which adversely recognizes the best candidate building in the local map. Our model is developed based on kernel density estimation that helps reduce noise in the smartphone sensors, such as GPS readings and camera ray direction reported by noisy accelerometer and compass. Experimental results demonstrate that our approach recognizes buildings ranging from 20m to 520m and achieves 92.7% accuracy in downtown areas where the Manhattan World Assumption is applicable. In addition, the processing time is no more than 6 seconds for 87% of cases. Compared with existing building localization schemes, SmartGuide offers numerous advantages. Our method avoids taking multiple photos, intricate 3D reconstruction or any initial deployment cost of database construction, making it faster and less labor-intensive than existing solutions.
Keywords: building localization, mobile computing, single image, smartphone (ID#: 15-6868)
URL: http://doi.acm.org/10.1145/2746285.2746294
Muyuan Li, Haojin Zhu, Zhaoyu Gao, Si Chen, Le Yu, Shangqian Hu, Kui Ren; “All Your Location Are Belong to Us: Breaking Mobile Social Networks for Automated User Location Tracking,” in MobiHoc ’14 Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing, August 2014, Pages 43–52. doi:10.1145/2632951.2632953
Abstract: Location-based social networks (LBSNs) feature friend discovery by location proximity that has attracted hundreds of millions of users world-wide. While leading LBSN providers claim the well-protection of their users’ location privacy, for the first time we show through real world attacks that these claims do not hold. In our identified attacks, a malicious individual with the capability of no more than a regular LBSN user can easily break most LBSNs by manipulating location information fed to LBSN client apps and running them as location oracles. We further develop an automated user location tracking system and test it on leading LBSNs including Wechat, Skout, and Momo. We demonstrate its effectiveness and efficiency via a 3 week real-world experiment on 30 volunteers and show that we could geo-locate any target with high accuracy and readily recover his/her top 5 locations. Finally, we also develop a framework that explores a grid reference system and location classifications to mitigate the attacks. Our result serves as a critical security reminder of the current LBSNs pertaining to a vast number of users
Keywords: location privacy, mobile social network (ID#: 15-6869)
URL: http://doi.acm.org/10.1145/2632951.2632953
Haiming Jin, Lu Su, Danyang Chen, Klara Nahrstedt, Jinhui Xu; “Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems,” in MobiHoc ’15 Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, June 2015, Pages 167–176. doi:10.1145/2746285.2746310
Abstract: Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users’ quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the single-minded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-to-optimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.
Keywords: crowd sensing, incentive mechanism, quality of information (ID#: 15-6870)
URL: http://doi.acm.org/10.1145/2746285.2746310
Divya Saxena, Vaskar Raychoudhury, Nalluri SriMahathi; “SmartHealth-NDNoT: Named Data Network of Things for Healthcare Services,” in MobileHealth ’15 Proceedings of the 2015 Workshop on Pervasive Wireless Healthcare, June 2015, Pages 45–50. doi:10.1145/2757290.2757300
Abstract: In recent years, healthcare sector has emerged as a major application area of Internet-of-Things (IoT). IoT aims to automate healthcare services through remote monitoring of patients using several vital sign sensors. Remotely collected patient records are then conveyed to the hospital servers through the user’s smartphones. Healthcare IoT can thus reduce a lot of overhead while allowing people to access healthcare services all the time and everywhere. However, healthcare IoT exchanges data over the IP-centric Internet which has vulnerabilities related to security, privacy, and mobility. Those features are added to the Internet as external add-ons. In order to solve this problem, in this paper, we propose to use Named Data Networking (NDN), which is a future Internet paradigm based on Content-Centric Networking (CCN). NDN has in-built support for user mobility which is well-suited for mobile patients and caregivers. NDN also ensures data security instead of channel security earlier provided by the Internet. In this paper, we have developed NDNoT, which is an IoT solution for smart mobile healthcare using NDN. Our proof-of-concept prototype shows the usability of our proposed architecture.
Keywords: healthcare, internet of things (iot), named data networking (ndn), ndnot, open mhealth architecture (ID#: 15-6871)
URL: http://doi.acm.org/10.1145/2757290.2757300
Note:
Articles listed on these Pages have been found on publicly available internet Pages and are cited with links to those Pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to news@scienceofsecurity.net for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.