Relevant bibliographies by topics / Fingerprinting

Academic literature on the topic 'Fingerprinting'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Fingerprinting"

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Gowda, Ashmitha. "Brain Fingerprinting." International Journal of Research Publication and Reviews 4, no. 5 (May 4, 2023): 1707–10. http://dx.doi.org/10.55248/gengpi.234.5.40436.

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Setiabudi, Christian Alvin, and Gede Putra Kusuma. "Performance Evaluation of Multilateration and Fingerprinting Method in Indoor Positioning System." International Journal of Emerging Technology and Advanced Engineering 11, no. 10 (October 15, 2021): 143–52. http://dx.doi.org/10.46338/ijetae1021_18.

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Indoor Positioning System has been one of the most attractive research after Bluetooth Low Energy (BLE) was introduced. This technology mainly used because of the reduction of material and energy cost over time that has huge impact compared to other technologies, which are more costly. Most recent research resolve around improving the accuracy of calculated position of the user by implementing different method to enable an indoor positioning system, and to remove any noises in the dataset. This paper objective is to compare some of the available methods that are used to enable Indoor Positioning System such as Fingerprinting, Multilateration, Trilateration, and Heron Bilateration. Since the performance of Fingerprinting is better compared to other methods, we combine Fingerprinting’s offline phase with the other methods to create a hybrid method and compare the accuracy of predicted user’s position. The experimental results show that the Fingerprinting and WKNN method outperform all other methods by resulting on 271.76 cm mean of error.
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Smolens, Jared C., Brian T. Gold, Jangwoo Kim, Babak Falsafi, James C. Hoe, and Andreas G. Nowatzyk. "Fingerprinting." ACM SIGPLAN Notices 39, no. 11 (November 2004): 224–34. http://dx.doi.org/10.1145/1037187.1024420.

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Smolens, Jared C., Brian T. Gold, Jangwoo Kim, Babak Falsafi, James C. Hoe, and Andreas G. Nowatzyk. "Fingerprinting." ACM SIGARCH Computer Architecture News 32, no. 5 (December 2004): 224–34. http://dx.doi.org/10.1145/1037947.1024420.

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Smolens, Jared C., Brian T. Gold, Jangwoo Kim, Babak Falsafi, James C. Hoe, and Andreas G. Nowatzyk. "Fingerprinting." ACM SIGOPS Operating Systems Review 38, no. 5 (December 2004): 224–34. http://dx.doi.org/10.1145/1037949.1024420.

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Garcia, David, and Karla Miño. "DNA fingerprinting." Bionatura 2, no. 4 (December 15, 2017): 477–80. http://dx.doi.org/10.21931/rb/2017.02.04.12.

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Brown, George B. "DNA Fingerprinting." Science 247, no. 4946 (March 2, 1990): 1018–19. http://dx.doi.org/10.1126/science.247.4946.1018.c.

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Sarkar, Gobinda. "DNA Fingerprinting." Science 247, no. 4946 (March 2, 1990): 1018. http://dx.doi.org/10.1126/science.247.4946.1018.b.

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Kumar, Sanjay. "DNA Fingerprinting." Science 247, no. 4946 (March 2, 1990): 1019. http://dx.doi.org/10.1126/science.247.4946.1019.a.

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Brown, George B. "DNA Fingerprinting." Science 247, no. 4946 (March 2, 1990): 1018–19. http://dx.doi.org/10.1126/science.247.4946.1018-c.

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Dissertations / Theses on the topic "Fingerprinting"

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Strobel, Cornelia. "Fuzzy Fingerprinting." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200500106.

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Fingerprints play an important role in biometrics and cryptography. Their creation might be based on one-way hash functions, which should usually also be collision-resistant. But users tend to draw less attention at those fingerprints - so an attacker might hand out a similar fingerprint in order to spoof identity. The main ideas for creating such 'fuzzy fingerprints' and the creation algorithm itself are discussed in this lecture. The demonstration of the tool, that produces fuzzy fingerprints shows the practical background of this technique
Fingerabdrücke besitzen sowohl in der Kryptographie als auch in der Biometrie eine große Bedeutung. In kryptographischen Anwendungen werden diese durch Einweg-Hash-Verfahren erzeugt, die für bestimmte Anwendungen auch kollisionsresitent sein müssen. In der Praxis schenken Benutzer diesen Fingerprints weit weniger Aufmerksamkeit - oft genügt es nur hinreichend ähnliche Fingerprints auszugeben, um die Nutzer zu täuschen Die Kriterien, die dabei erfüllt sein müssen und die Erzeugung dieser "Fuzzy Fingerprints" sind Hauptbestandteil dieses Vortrags. Durch die Demonstration eines Tools im praktischen Einsatz wird dieser abgeschlossen
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Kristoffer, Frisell. "FINGERPRINTING AV HÅRDVARULIKA ENHETER : Precisionsmätning med fingerprinting på mobila enheter." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-10052.

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Ellch, Jonathan P. "Fingerprinting 802.11 devices." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FEllch.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Dennis Volpano and Chris Eagle. "September 2006." Includes bibliographical references (p. 67). Also available in print.
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Kim, Joonsoo. "Reliable SRAM Fingerprinting." Thesis, The University of Texas at Austin, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3572874.

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Device identification, as human identification has been, has become critical to mitigate growing security problems. In the era of ubiquitous computing, it is important to ensure universal device identities that are versatile in number of ways, for example, to enhance computer security or to enable large-scale data capture, management and analysis. For device identities, simple labeling works only if they are properly managed under a highly controlled environment. We can also impose hard-coded serial numbers into non-volatile memories but it is well known that this is expensive and vulnerable to security attacks. Hence, it is desirable to develop reliable and secure device identification methods using fingerprint-like characteristics of the electronic devices.

As technology scales, process variation has become the most critical barrier to overcome for modern chip development. Ironically, there are some research works to exploit the aggressive process variation for the identification of individual devices. They find measurable physical characteristics that are unique to each integrated circuit. Among them, device identification using initial power-up values of SRAM cells, called SRAM fingerprints, has been emphasized lately in part due to the abundant availability of SRAM cells in modern microprocessors. More importantly, since the cross-coupled inverter structure of each SRAM cell amplifies even the small mismatches between two inverter nodes, it is thus very sensitive to and maximizes the effect of random process variation, making SRAM fingerprints to acquire great features as a naturally inherent device ID.

Therefore, this work focuses on achieving reliable device identification using SRAM fingerprints. As of date, this dissertation shows the most comprehensive feature characterization of SRAM fingerprints based on the large datasets measured from the real devices under various environmental conditions. SRAM fingerprints in three different process technologies—IBM 32nm SOI technology, IBM 65nm bulk technology, and TSMC 90nm low-k dielectric technology—have been investigated across different temperatures or voltages. By using formal statistical tools, the required features for SRAM fingerprints necessary to be usable as device IDs—uniqueness, randomness, independence, reproducibility, etc.—have been empirically proven.

As some of the previous works mentioned, there is an inherent unreliability of the initial states of SRAM cells so that there is always some chance of errors during identification process. It is observed that, under environmental variations, the instability aggravates even more. Most of the previous work, however, ignores the temperature dependence of the SRAM power-up values, which turns out to be critical against our past speculations and becomes a real challenge in realizing a reliable SRAM-based device identification. Note that temperature variation will not be negligible in many situations, for example, authentication of widely distributed sensors.

We show that it is possible to achieve SRAM-based device identification system that reliably operates under a wide range of temperatures. The proposed system is composed of three major steps: enrollment, system evaluation, and matching. During the enrollment process, power-up samples of SRAM fingerprints are captured from each manufactured device and the feature information or characterization identifier (CID) is characterized to generate a representative fingerprint value associated with the product device. By collecting the samples and the CIDs, system database gets constructed before distributing devices to the field. During the matching process, we take a single sample fingerprint of a power-cycle experiment, the field identifier (FID), and perform a match against a repository of CID’s of all manufactured devices. There is an additional monitoring subsystem, called system evaluation, that estimates the system accuracy with the system database. It controls the system parameters while maintaining the system accuracy requirement.

This work delivers a total-package statistical framework that raises design issues of each step and provides systematic solutions to deal with these inter-related issues. We provide statistical methods to determine sample size for the enrollment of chip identities, to generate the representative fingerprint features with the limited number of test samples, and to estimate the system performance along with the proposed system parameter values and the confidence interval of the estimation. A novel matching scheme is proposed to improve the system accuracy and increase population coverage under environmental variations, especially temperature variation. Several advanced mechanisms to exploit the instability for our benefit is also discussed along with supporting state-of-the-art circuit technologies. All these pioneering theoretical frameworks have been validated by the comprehensive empirical analysis based on the real SRAM fingerprint datasets introduced earlier.

The main contribution here is that this work provides a comprehensive interdisciplinary framework to enable reliable SRAM fingerprinting, even if the fingerprint, depending on ambient conditions, exhibits nondeterministic behaviors. Furthermore, the interdisciplinary bases introduced in our work are expected to provide generic fundamental methodologies that apply to device fingerprints in general, not just to SRAM fingerprints. (Abstract shortened by UMI.)

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MA, DAN. "Magnetic Resonance Fingerprinting." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1426170542.

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Liu, Hui Qing 1957. "Fingerprinting biological materials." Thesis, The University of Arizona, 1992. http://hdl.handle.net/10150/291369.

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A study for non-destructive measurement and analysis of agricultural products was undertaken. Some enhancements were made to a spectrophotometer, DK-2A to meet the requirements of low uniform error band when using the factor analysis method. The instrument was modified by adding an integrating sphere, optical transducer, and electronic interface linked to a computer. The instrument was calibrated using targets traceable to NITS standards of BaSO₄ with mixtures of Carbon Black. This gave a range of known reflectance and provided consistent and reproducible data every 5 nm wavelengths between 350 nm and 800 nm. The spectral wavelength bands of the different biological materials were studied with target factor analysis. Target factors were applied in biological materials to characterize the important spectral properties. Pigment of foods, chlorophyll, carotenoid and myoglobin were separated from the spectral response of oranges, apples and meat. Their respective spectral signatures were determined.
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Karlsson, Anna. "Device Sensor Fingerprinting : Mobile Device Sensor Fingerprinting With A Biometric Approach." Thesis, Linköpings universitet, Informationskodning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119341.

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The number of connected devices connected to the Internet is growing rapidly. When talking about devices it also covers the ones not having any contact with humans. This type of devices are the ones that are expected to increase the most. That is why the field of device fingerprinting is an area that requires further investigation. This thesis measures and evaluates the accelerometer, camera and gyroscope sensor of a mobile device to the use as device fingerprinting. The method used is based on previous research in sensor identification together with methods used for designing a biometric system. The combination with long-proven methods in the biometric area with new research of sensor identification is a new approach of looking at device fingerprinting.
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Löfvenberg, Jacob. "Codes for digital fingerprinting /." Linköping : Univ, 2001. http://www.bibl.liu.se/liupubl/disp/disp2001/tek722s.pdf.

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Lindkvist, Tina. "Fingerprinting of digital documents /." Linköping : Univ, 2001. http://www.bibl.liu.se/liupubl/disp/disp2001/tek706s.pdf.

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Porter, Alastair. "Evaluating musical fingerprinting systems." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117191.

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Audio fingerprinting is a process that uses computers to analyse small clips of music recordings to answer a common question that people who listen to music often ask : "What is the name of that song I hear ?" Audio fingerprinting systems identify musical content in audio and search a reference database for recordings that contain the same musical features. These systems can find matching recordings even when the query has been recorded in a public space and contains added noise. Different audio fingerprinting algorithms are better at identifying different types of queries, for example, queries that are short, or have a large amount of noise present in the signal. There are few comprehensive comparisons of fingerprinting systems available in the literature that compare the retrieval accuracy offingerprinting systems with a wide range of querys.This thesis presents an overview of the historical developments in audio fingerprinting, including an analysis of three state-of-the-art audio fingerprinting algorithms. The thesis introduces factors that must be considered when performing a comparative evaluation of many fingerprinting algorithms, and presents a new evaluation framework that has been developed to address these factors. The thesis contributes the results of a large-scale comparison between three audio fingerprinting algorithms, with an analysis recommending which algorithms should be used to identify music queries recorded in different situations.
Le système d'empreinte audio est un procédé qui analyse de courts extraits de musique avec un ordinateur pour répondre à une question courante: « Quelle est le nom de cette chanson que j'écoute? ». Les systèmes d'empreintes audio identifient le contenu musical d'un enregistrement et cherchent des documents sonores possédant les même traits musicaux au sein d'une base de données de référence. Ces systèmes sont capables de fonctionner même si les requêtes qui leur sont transmises sont enregistrées dans un espace public, avec de nombreuses sources de bruit extérieur. Les différents algorithmes d'empreinte audio se distinguent par le type de requête qu'ils peuvent traiter: certains se concentrent sur des requêtes de courte durée, d'autres sont optimisés pour pouvoir être performant même dans des conditions de bruit très défavorables. Dans la littérature, il existe peu d'études comparatives poussées traitant spécifiquement des performances des systèmes de reconnaissance par empreinte audio dans un large éventail de cas.Cette thèse présente une vue d'ensemble de l'histoire du développement des systèmes d'empreinte audio. Cette thèse introduit en suite des facteurs qui doivent être pris en compte lors de l'évaluation comparative de plusieurs algorithmes pour la reconnaissance par empreinte audio. De plus, ce travail présente un nouveau cadre d'évaluation développé afin d'incorporer ces facteurs. Cette thèse combine les résultats d'une comparaison à grande échelle de trois algorithmes d'identification d'empreinte audio avec une analyse recommandant lequel de ces algorithmes est le plus efficace pour identifier la plus grande variété d'extraits audio.
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Books on the topic "Fingerprinting"

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America, Boy Scouts of. Fingerprinting. 2nd ed. Irving, Tex: Boy Scouts of America, 2003.

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Ahouse, Jeremy John. Fingerprinting. Berkeley, Calif: Great Explorations in Math and Science (GEMS), Lawrence Hall of Science, 1989.

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Kirby, Lorne T. DNA Fingerprinting. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6.

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Wang, Cliff, Ryan M. Gerdes, Yong Guan, and Sneha Kumar Kasera, eds. Digital Fingerprinting. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6601-1.

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J, Schmidtke, ed. DNA fingerprinting. 2nd ed. Oxford: BIOS Scientific, 1998.

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Krawczak, Michael. DNA fingerprinting. Oxford, UK: Bios Scientific Publishers, 1994.

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DNA fingerprinting. New York: F. Watts, 1991.

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Ahouse, Jeremy John. Fingerprinting: Teacher's guide. Berkeley, CA: Lawrence Hall of Science, University of California, 1987.

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Kirby, Lorne T. DNA fingerprinting: An introduction. New York: W.H. Freeman, 1992.

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Kirby, Lorne T. DNA fingerprinting: An introduction. New York: Oxford University Press, 1997.

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Book chapters on the topic "Fingerprinting"

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Krafsur, E. S., R. D. Moon, R. Albajes, O. Alomar, Elisabetta Chiappini, John Huber, John L. Capinera, et al. "Fingerprinting." In Encyclopedia of Entomology, 1429. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_3808.

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Barg, Alexander, and Gregory Kabatiansky. "Fingerprinting." In Encyclopedia of Cryptography and Security, 465–67. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_381.

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Dietzfelbinger, Martin. "Fingerprinting." In Algorithms Unplugged, 181–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15328-0_19.

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HromkoviČ, Juraj. "Fingerprinting." In Texts in Theoretical Computer Science An EATCS Series, 131–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-27903-2_4.

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Kirby, Lorne T. "Introduction." In DNA Fingerprinting, 1–5. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6_1.

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Melson, Kenneth E. "Legal and Ethical Considerations." In DNA Fingerprinting, 189–215. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6_10.

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Kirby, Lorne T. "Case Applications." In DNA Fingerprinting, 217–59. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6_11.

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Kirby, Lorne T. "Genetic Principles." In DNA Fingerprinting, 7–34. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6_2.

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Kirby, Lorne T. "Laboratory Organization." In DNA Fingerprinting, 35–50. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6_3.

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Kirby, Lorne T. "Specimens." In DNA Fingerprinting, 51–74. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12040-6_4.

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Conference papers on the topic "Fingerprinting"

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Smolens, Jared C., Brian T. Gold, Jangwoo Kim, Babak Falsafi, James C. Hoe, and Andreas G. Nowatzyk. "Fingerprinting." In the 11th international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1024393.1024420.

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Kobusińska, Anna, Jerzy Brzeziński, and Kamil Pawulczuk. "Device Fingerprinting: Analysis of Chosen Fingerprinting Methods." In 2nd International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006375701670177.

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Sirinam, Payap, Mohsen Imani, Marc Juarez, and Matthew Wright. "Deep Fingerprinting." In CCS '18: 2018 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3243734.3243768.

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Wang, Chenggang, Jimmy Dani, Xiang Li, Xiaodong Jia, and Boyang Wang. "Adaptive Fingerprinting." In CODASPY '21: Eleventh ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3422337.3447835.

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Gil, Joseph, Alexander Gorovoy, and Alon Itai. "Software Fingerprinting." In 2006 International Conference on Information Technology: Research and Education. IEEE, 2006. http://dx.doi.org/10.1109/itre.2006.381536.

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Khakpour, Amir R., Joshua W. Hulst, Zihui Ge, Alex X. Liu, Dan Pei, and Jia Wang. "Firewall fingerprinting." In IEEE INFOCOM 2012 - IEEE Conference on Computer Communications. IEEE, 2012. http://dx.doi.org/10.1109/infcom.2012.6195544.

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Herrmann, Dominik, Rolf Wendolsky, and Hannes Federrath. "Website fingerprinting." In the 2009 ACM workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1655008.1655013.

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Houser, Rebekah L., Willett Kempton, Rodney McGee, Fouad Kiamilev, and Nick Waite. "EV Fingerprinting." In WCX™ 17: SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2017. http://dx.doi.org/10.4271/2017-01-1700.

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Vanaubel, Yves, Jean-Jacques Pansiot, Pascal Mérindol, and Benoit Donnet. "Network fingerprinting." In IMC'13: Internet Measurement Conference. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2504730.2504761.

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Harrison, Chris, Munehiko Sato, and Ivan Poupyrev. "Capacitive fingerprinting." In the 25th annual ACM symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2380116.2380183.

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Reports on the topic "Fingerprinting"

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Daugherty, Patrick. Rapid Molecular Fingerprinting of Pathogens. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada455360.

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Jackson, S. E., B. Dubé, P. Mercier-Langevin, and D. Rhys. Fingerprinting ore processes in auriferous systems. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/299583.

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Peisert, Sean. Fingerprinting Communication and Computation on HPC Machines. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/983323.

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Echt, Craig, and Sedley Josserand. DNA fingerprinting sets for four southern pines. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2018. http://dx.doi.org/10.2737/srs-rn-24.

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Echt, Craig, and Sedley Josserand. DNA fingerprinting sets for four southern pines. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2018. http://dx.doi.org/10.2737/srs-rn-24.

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Gupta, Shweta. DNA Fingerprinting: A Major Tool for Crime Investigation. Spring Library, April 2021. http://dx.doi.org/10.47496/nl.blog.24.

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DNA profiling has revolutionized the criminal justice system over the past decades. It has even enabled the law enforcement from exonerating people who have been convicted wrongfully of crimes which they did not commit.
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Poulin, R. S., A. M. McDonald, D. J. Kontak, and M. B. McClenaghan. Scheelite geochemical signatures and potential for fingerprinting ore deposits. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2015. http://dx.doi.org/10.4095/296473.

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Stetzenbach, K., and K. Johannesson. Fingerprinting of ground water by ICP-MS. Final report. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/239302.

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Bischof, Laura. DNA fingerprinting analysis of captive Asian elephants, Elephas maximas. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5850.

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Winston Chen, C. H., N. I. Taranenko, Y. F. Zhu, C. N. Chung, and S. L. Allman. Laser mass spectrometry for DNA sequencing, disease diagnosis, and fingerprinting. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/446348.

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