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Spatiotemporal Aspects of Big Data

Perspective,” Int. J. Comput. Trends Technol. , vol. 19, no. 1, pp. 9–14, 2015. [22] “What is Samza?,” Apache Software Foundation. [Online]. Available: . [Accessed: 04-Dec-2017]. [23] P. Sams, Selenium Essentials . Packt Publishing Limited, 2015. [24] “Apache SparkTM - Unified Analytics Engine for Big Data,” Apache Software Foundation. [Online]. Available: . [Accessed: 04-Dec-2017]. [25] A. G. Shoro, and S. & T. R. Soomro, “Big Data Analysis: Ap

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Comparing Transformation Possibilities of Topological Functioning Model and BPMN in the Context of Model Driven Architecture

R eferences [1] J. Miller and J. Mukerji, (2003, June 12). MDA Guide Version 1.0.1 . [Online]. Available: [February 10, 2016]. [2] J. Osis, “Software Development with Topological Model in the Framework of MDA,” in Proc. of the 9th CAiSE/IFIP8.1/EUNO Int. Workshop on Evaluation of Modeling Methods in Systems Analysis and Design (EMMSAD’2004) in connection with the CAiSE’2004 . vol. 1, Riga: RTU, 2004, pp. 211–220. [3] J. Osis and E. Asnina, “Is Modeling a Treatment for the Weakness of Software Engineering

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UML Sequence Diagram: Transformation from the Two-Hemisphere Model and Layout

International Conference on Evaluation of Novel Approaches to Software Engineering, June 8-11, 2011, Beijing, China. SciTePress Digital Library 2011. [18] T. Poranen, E. Makinen and J. Nummenmaa, How to Draw a Sequence Diagram : SPLST'03 Proceedings of the Eighth Symposium on Programming Languages and Software Tools, June 17-18, 2003, Kuopio, Finland. University of Kuopio, Department of Computer Science 2003. [19] B.E. Goldstein, Sensation and Perception. Wadsworth, 2002. [20] [Battista u.c.1999] G. di Battista, P. Eades

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No Place to Hide: Inadvertent Location Privacy Leaks on Twitter

. Huguenin, U. Hengartner, and J.-P. Hubaux. On the privacy implications of location semantics. Proceedings on Privacy Enhancing Technologies , 2016(4):165–183, 2016. [5] M. Allen. Health Insurers Are Vacuuming Up Details About You – And It Could Raise Your Rates, NPR. , 2018. [6] J. Bakerman, K. Pazdernik, A. Wilson, G. Fairchild, and R. Bahran. Twitter geolocation: A hybrid approach. ACM Transactions on Knowledge

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Blocking-resistant communication through domain fronting

. Mathewson. Pluggable transport specification, Oct. 2010. . [6] ASL19 and Psiphon. Information controls: Iran’s presidential elections. Technical report, 2013. . [7] D. J. Bernstein, T. Lange, and P. Schwabe. Public-key authenticated encryption: crypto_box, Aug. 2010. . [8] B. Boe. Bypassing Gogo’s inflight Internet authentication, Mar. 2012.

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SoK: Privacy on Mobile Devices – It’s Complicated

References [1] I. Leontiadis, C. Efstratiou, M. Picone, and C. Mascolo, “Don’t kill my ads!: balancing privacy in an ad-supported mobile application market,” in MobiSys 2012. [2] B. Ur, P. G. Leon, L. F. Cranor, R. Shay, and Y. Wang, “Smart, useful, scary, creepy: perceptions of online behavioral advertising,” in SOUPS 2012. [3] Z. Xu, K. Bai, and S. Zhu, “Taplogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors,” in WiSec 2012. [4] L. Cai and H. Chen

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Differentially Private Oblivious RAM

Census Data Release. . Published: Summer 2016. [5] Why the Census Bureau Adopted Differential Privacy for the 2020 Census of Population. . [6] IBM systems cryptographic hardware products. , 2016. [7] Michael Backes, Aniket Kate, Matteo Maffei, and Kim Pecina. Obliviad: Provably secure and practical online behavioral advertising. In

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Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

Systems and Applications , HotMobile ’11, New York, NY, USA, 2011. ACM. [14] Igor Bilogrevic, Kévin Huguenin, Berker Agir, Murtuza Jadliwala, Maria Gazaki, and Jean-Pierre Hubaux. A machine-learning based approach to privacy-aware information-sharing in mobile social networks. Pervasive and Mobile Computing , 25, 2016. [15] V. Bindschaedler and R. Shokri. Synthesizing plausible privacy-preserving location traces. In 2016 IEEE Symposium on Security and Privacy (SP) , May 2016. [16] Kenneth Block, Sashank Narain, and Guevara Noubir. An autonomic and

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Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

signatures: Formal definitions, simplified requirements, and a construction based on general assumptions. In EUROCRYPT ’03 , pages 614–629, 2003. [21] Daniel Slamanig, Raphael Spreitzer, and Thomas Unterluggauer. Group signatures with linking-based revocation: A pragmatic approach for efficient revocation checks. In MyCrypt 2016 , 2016. to appear. [22] Julien Bringer and Alain Patey. Backward unlinkability for a VLR group signature scheme with efficient revocation check. IACR Cryptology ePrint Archive , 2011:376, 2011. [23] Toru Nakanishi, Hiroki Fujii

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Flying Eyes and Hidden Controllers: A Qualitative Study of People’s Privacy Perceptions of Civilian Drones in The US

.1145/2696454.2696484 [10] John Travis Butler and Arvin Agah. 2001. Psychological effects of behavior patterns of a mobile personal robot. Autonomous Robots 10, 2 (2001), 185-202. [11] Ryan Calo. 2011. The drone as privacy catalyst. Stanford Law Review Online 64 (2011), 29-33. [12] Ann Cavoukian. 2012. Privacy and drones: Unmanned aerial vehicles. Information and Privacy Commissioner of Ontario, Canada. [13] Reece A Clothier, Dominique A Greer, Duncan G Greer, and Amisha M Mehta. 2015. Risk perception and the public acceptance of drones

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