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Статті в журналах з теми "Location- Privacy control"
Yunxiang, Zhang, and Wang Bin. "Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection." Wireless Communications and Mobile Computing 2023 (January 30, 2023): 1–8. http://dx.doi.org/10.1155/2023/4792248.
Повний текст джерелаPeng, Tao, Qin Liu, and Guojun Wang. "Enhanced Location Privacy Preserving Scheme in Location-Based Services." IEEE Systems Journal 11, no. 1 (March 2017): 219–30. http://dx.doi.org/10.1109/jsyst.2014.2354235.
Повний текст джерелаZhu, Liang, Xiaowei Liu, Zhiyong Jing, Liping Yu, Zengyu Cai, and Jianwei Zhang. "Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks." Electronics 12, no. 1 (December 24, 2022): 70. http://dx.doi.org/10.3390/electronics12010070.
Повний текст джерелаYang, Guangcan, Shoushan Luo, Yang Xin, Hongliang Zhu, Jingkai Wang, Mingzhen Li, and Yunfeng Wang. "A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks." Applied Sciences 10, no. 23 (November 25, 2020): 8402. http://dx.doi.org/10.3390/app10238402.
Повний текст джерелаPerusco, Laura, and Katina Michael. "Control, trust, privacy, and security: evaluating location-based services." IEEE Technology and Society Magazine 26, no. 1 (2007): 4–16. http://dx.doi.org/10.1109/mtas.2007.335564.
Повний текст джерелаLuceri, Luca, Davide Andreoletti, Massimo Tornatore, Torsten Braun, and Silvia Giordano. "Measurement and control of geo-location privacy on Twitter." Online Social Networks and Media 17 (May 2020): 100078. http://dx.doi.org/10.1016/j.osnem.2020.100078.
Повний текст джерелаChu, Xiang, Jun Liu, Daqing Gong, and Rui Wang. "Preserving Location Privacy in Spatial Crowdsourcing Under Quality Control." IEEE Access 7 (2019): 155851–59. http://dx.doi.org/10.1109/access.2019.2949409.
Повний текст джерелаQi He, Dapeng Wu, and P. Khosla. "The quest for personal control over mobile location privacy." IEEE Communications Magazine 42, no. 5 (May 2004): 130–36. http://dx.doi.org/10.1109/mcom.2004.1299356.
Повний текст джерелаGóes, Rômulo Meira, Blake C. Rawlings, Nicholas Recker, Gregory Willett, and Stéphane Lafortune. "Demonstration of Indoor Location Privacy Enforcement using Obfuscation." IFAC-PapersOnLine 51, no. 7 (2018): 145–51. http://dx.doi.org/10.1016/j.ifacol.2018.06.293.
Повний текст джерелаXu, Chuan, Li Luo, Yingyi Ding, Guofeng Zhao, and Shui Yu. "Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks." IEEE Wireless Communications Letters 9, no. 10 (October 2020): 1633–37. http://dx.doi.org/10.1109/lwc.2020.2999524.
Повний текст джерелаДисертації з теми "Location- Privacy control"
Jedrzejczyk, Lukasz. "Supporting location privacy management through feedback and control." Thesis, Open University, 2012. http://oro.open.ac.uk/36211/.
Повний текст джерелаCerf, Sophie. "control theory for computing systems : application to big-data cloud services & location privacy protection." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT024.
Повний текст джерелаThis thesis presents an application of Control Theory for Computing Systems. It aims at investigating techniques to build and control efficient, dependable and privacy-preserving computing systems. Ad-hoc service configuration require a high level of expertise which could benefit from automation in many ways. A control algorithm can handle bigger and more complex systems, even when they are extremely sensitive to variations in their environment. However, applying control to computing systems raises several challenges, e.g. no physics governs the applications. On one hand, the mathematical framework provided by control theory can be used to improve automation and robustness of computing systems. Moreover, the control theory provides by definition mathematical guarantees that its objectives will be fulfilled. On the other hand, the specific challenges of such use cases enable to expand the control theory itself. The approach taken in this work is to use two application computing systems: location privacy and cloud control. Those two use-cases are complementary in the nature of their technologies and softwares, their scale and in their end-users.The widespread of mobile devices has fostered the broadcasting and collection of users’ location data. It could be for the user to benefit from a personalized service (e.g. weather forecast or route planning) or for the service provider or any other third party to derive useful information from the mobility databases (e.g. road usage frequency or popularity of places). Indeed, many information can be retrieved from location data, including highly sensitive personal data. To overcome this privacy breach, Location Privacy Protection Mechanisms (LPPMs) have been developed. They are algorithm that modify the user’s mobility data, hopefully to hide some sensitive information. However, those tools are not easily configurable by non experts and are static processes that do not adapt to the user’s mobility. We develop two tools, one for already collected databases and one for online usage, that, by tuning the LPPMs, guarantee to the users objective-driven levels of privacy protection and of service utility preservation. First, we present an automated tool able to choose and configure LPPMs to protect already collected databases while ensuring a trade-off between privacy protection and database processing quality. Second, we present the first formulation of the location privacy challenge in control theory terms (plant and control, disturbance and performance signals), and a feedback controller to serve as a proof of concept. In both cases, design, implementation and validation has been done through experiments using data of real users collected on the field.The surge in data generation of the last decades, the so-called bigdata, has lead to the development of frameworks able to analyze them, such as the well known MapReduce. Advances in computing practices has also settled the cloud paradigms (where low-level resources can be rented to allow the development of higher level application without dealing with consideration such as investment in hardware or maintenance) as premium solution for all kind of users. Ensuring the performances of MapReduce jobs running on clouds is thus a major concern for the big IT companies and their clients. In this work, we develop advanced monitoring techniques of the jobs execution time and the platform availability by tuning the resource cluster size and realizing admission control, in spite of the unpredictable client workload. In order to deal with the non linearities of the MapReduce system, a robust adaptive feedback controller has been designed. To reduce the cluster utilization (leading to massive financial and energetic costs), we present a new event-based triggering mechanism formulation combined with an optimal predictive controller. Evaluation is done on a MapReduce benchmark suite running on a large-scale cluster, and using real jobs workloads
Chini, Foroushan Amir Hossein. "Protecting Location-Data Against Inference Attacks Using Pre-Defined Personas." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-66792.
Повний текст джерелаArdagna, C. A. "Privacy and security in distributed and pervasive systems." Doctoral thesis, Università degli Studi di Milano, 2008. http://hdl.handle.net/2434/55664.
Повний текст джерелаLin, Yousi. "Spectrum Management Issues in Centralized and Distributed Dynamic Spectrum Access." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104362.
Повний текст джерелаDoctor of Philosophy
Due to the rapid growth in wireless communication demands, the frequency spectrum is becoming increasingly crowded. Traditional spectrum allocation policy gives the unshared access of fixed bands to the licensed users, and there is little unlicensed spectrum left now to allocate to newly emerged communication demands. However, studies on spectrum occupancy show that many licensed users who own the license of certain bands are only active for a small percentage of time, which results in plenty of underutilized spectrum. Hence, a new spectrum sharing paradigm, called dynamic spectrum access (DSA), is proposed to mitigate this problem. DSA enables the spectrum sharing between different classes of users, generally, the unlicensed users in the DSA system can access the licensed spectrum opportunistically without interfering with the licensed users. Based on architecture design, DSA systems can be categorized as centralized and distributed. In centralized systems, a central controller will make decisions on spectrum usage for all unlicensed users. Whereas in distributed systems, unlicensed users can make decisions for themselves independently. To successfully enable DSA, both centralized and distributed DSA systems need to deal with spectrum management issues, such as resource allocation problems and user privacy issues, etc. The resource allocation problems include, for example, the problems to discover and allocate idle bands and the problems to control users' transmit power for successful coexistence. Privacy issues may also arise during the spectrum management process since certain information exchange is inevitable for global decision making. However, due to the Federal Communications Commission's (FCC) regulation, licensed users' privacy such as their location information must be protected in any case. As a result, dynamic and efficient spectrum management techniques are necessary for DSA users. In this dissertation, we investigate the above-mentioned spectrum management issues in both types of DSA systems, specifically, the spectrum sensing challenges with licensed user location privacy issues in centralized DSA, and the spectrum sharing problems in distributed DSA systems. In doing so, we propose novel schemes for solving each related spectrum management problem and demonstrate their efficacy through the results from extensive evaluations and simulations. We believe that this dissertation provides insightful advice for DSA users to solve different spectrum management issues for enabling DSA implementation, and hence helps in a wider adoption of dynamic spectrum sharing.
Chu, Yu-Shan, and 朱育姍. "How Private Benefit of Control Affects Emerging Market Enterprises FDI Location Choices." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/74300086758241454361.
Повний текст джерела輔仁大學
金融與國際企業學系金融碩士班
103
Most of previous research focused on location selectivity by analyzing environmental factors or competitive advantages, and majority of the sample are in developed countries. However, with the rise of emerging markets, most of the companies have one or more control shareholders who can lead the business decisions. Due to this reason, those companies will consider the other factors that differ from the companies in developed countries when they face overseas investment decisions. Therefore, this study focuses on the companies in emerging markets, and analyzes the view of private benefit of control, investigates the relationship between private benefits and oversea investment locations choices. Our samples period is from 2009 to 2013. We examine whether private benefit will affect the choice of oversea investment locations. We discuss it from three aspects: ownership structure, transaction and management. Empirical results show that there is a negative relation between divergence in control and cash flow rights, related party transaction and earnings management. This means control shareholders may try to influence corporate to invest in the region with weak institutions because they want to protect their private benefits, and avoid the monitoring mechanism reducing the scope of private benefits. However, there are no significant relations between cash holding and external environment institution. We believe that besides consideration for investment demand, there are other purposes of cash holding. As a result, when the companies of emerging market face oversea investment decisions, private benefit is also one of the factor that will affect the location choices.
Книги з теми "Location- Privacy control"
Sushil, Jajodia, Samarati Pierangela, Wang X. Sean, and SpringerLink (Online service), eds. Privacy in Location-Based Applications: Research Issues and Emerging Trends. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
Знайти повний текст джерелаJordan, Frith, ed. Mobile interfaces in public spaces: Locational privacy, control, and urban sociability. New York, NY: Routledge, 2012.
Знайти повний текст джерелаInstitute of Medicine (U.S.). Committee on the Role of Institutional Review Boards in Health Services Research Data Privacy Protection. Protecting data privacy in health services research. Washington, D.C: National Academy Press, 2000.
Знайти повний текст джерелаFrith, Jordan, and Adriana de Souza e Silva. Mobile Interfaces in Public Spaces: Locational Privacy, Control, and Urban Sociability. Taylor & Francis Group, 2012.
Знайти повний текст джерелаFrith, Jordan, and Adriana de Souza e Silva. Mobile Interfaces in Public Spaces: Locational Privacy, Control, and Urban Sociability. Taylor & Francis Group, 2012.
Знайти повний текст джерелаMedicine, Institute of, Division of Health Care Services, and Committee on the Role of Institutional Review Boards in Health Services Research Data Privacy Protection. Protecting Data Privacy in Health Services Research. National Academies Press, 2000.
Знайти повний текст джерелаЧастини книг з теми "Location- Privacy control"
Ardagna, Claudio A., Marco Cremonini, Sabrina De Capitani di Vimercati, and Pierangela Samarati. "Access Control in Location-Based Services." In Privacy in Location-Based Applications, 106–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03511-1_5.
Повний текст джерелаSolomon, Michael G., Vaidy Sunderam, Li Xiong, and Ming Li. "Mutually Private Location Proximity Detection with Access Control." In Data and Applications Security and Privacy XXXI, 164–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61176-1_9.
Повний текст джерелаUlltveit-Moe, Nils, and Vladimir Oleshchuk. "Mobile Security with Location-Aware Role-Based Access Control." In Security and Privacy in Mobile Information and Communication Systems, 172–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30244-2_15.
Повний текст джерелаSmith, Ian, Giovanni Iachello, and Mika Raento. "Mobile HCI 2004 Workshop on Location Systems Privacy and Control." In Mobile Human-Computer Interaction - MobileHCI 2004, 525–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28637-0_72.
Повний текст джерелаPennekamp, Jan, Anastasiia Belova, Thomas Bergs, Matthias Bodenbenner, Andreas Bührig-Polaczek, Markus Dahlmanns, Ike Kunze, et al. "Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead." In Internet of Production, 1–25. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98062-7_2-1.
Повний текст джерелаVaidya, Tavish, and Micah Sherr. "Mind Your $$(R, \varPhi )$$ s: Location-Based Privacy Controls for Consumer Drones." In Security Protocols XXIII, 80–90. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26096-9_9.
Повний текст джерелаVaidya, Tavish, and Micah Sherr. "Mind Your $$(R, \varPhi )$$ s: Location-Based Privacy Controls for Consumer Drones (Transcript of Discussion)." In Security Protocols XXIII, 91–104. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26096-9_10.
Повний текст джерелаChen, Jiahang, and Jürgen Roßmann. "Integration of an IoT Communication Infrastructure in Distributed Production Systems in Industry 4.0." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, 367–77. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-10071-0_30.
Повний текст джерелаEldin, Amr Ali, and Zoran Stojanovic. "Privacy Control Requirements for Context-Aware Mobile Services." In Ubiquitous and Pervasive Computing, 1465–80. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-960-1.ch091.
Повний текст джерелаMagkos, Emmanouil. "Cryptographic Approaches for Privacy Preservation in Location-Based Services." In Systems Approach Applications for Developments in Information Technology, 273–97. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1562-5.ch019.
Повний текст джерелаТези доповідей конференцій з теми "Location- Privacy control"
Koufogiannis, Fragkiskos, and George J. Pappas. "Location-dependent privacy." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7799441.
Повний текст джерелаHongxia Jin, Gokay Saldamli, Richard Chow, and Bart P. Knijnenburg. "Recommendations-based location privacy control." In 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops 2013). IEEE, 2013. http://dx.doi.org/10.1109/percomw.2013.6529526.
Повний текст джерелаHeng Liu, Tiejun Wang, Ming Sun, Zhen Liu, and Mingtian Zhou. "Location privacy in sparse environment." In 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5486958.
Повний текст джерелаRama Krishna, T. Siva, L. Venkateswara Kiran, and P. Siva Prasad. "Privacy control on location and co-location in interdependent data." In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). IEEE, 2019. http://dx.doi.org/10.1109/vitecon.2019.8899664.
Повний текст джерелаYang, Tao, Cong Tang, Liangwen Yu, Wei Xin, Yong Deng, Jianbin Hu, and Zhong Chen. "VLSP: Enabling Location Privacy in Vehicular Location Based Services." In 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2011. http://dx.doi.org/10.1109/imccc.2011.121.
Повний текст джерелаZheng, Jiangyu, Xiaobin Tan, Cliff Zou, Yukun Niu, and Jin Zhu. "A cloaking-based approach to protect location privacy in location-based services." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6895872.
Повний текст джерелаWang, Na, and Haiyang Yu. "Generating Perturbations with Hilbert Curves and Differential Privacy for Location Privacy." In 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/mecae-17.2017.17.
Повний текст джерелаTong, Wei, and Xi Feng. "Location Privacy Protection and Location Verification Mechanism of Vehicle in VANET." In 2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC). IEEE, 2023. http://dx.doi.org/10.1109/itnec56291.2023.10082547.
Повний текст джерелаLei, Zhang, Yu Lili, Li Jing, and Meng Fanbo. "Location privacy protection algorithm based on correlation coefficient." In 2018 4th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2018. http://dx.doi.org/10.1109/iccar.2018.8384695.
Повний текст джерелаLakadkutta, Ahmed H. I., and R. V. Mante. "Location based privacy preserving access control for relational data." In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2016. http://dx.doi.org/10.1109/rteict.2016.7808206.
Повний текст джерелаЗвіти організацій з теми "Location- Privacy control"
Qi, Yan, Ryan Fries, Shambhu Saran Baral, and Pranesh Biswas. Evaluating the Costs and Benefits of Snow Fences in Illinois: Phase 2. Illinois Center for Transportation, November 2020. http://dx.doi.org/10.36501/0197-9191/20-020.
Повний текст джерела