The Experts below are selected from a list of 1422 Experts worldwide ranked by ideXlab platform
Stephen G Ritchie - One of the best experts on this subject based on the ideXlab platform.
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a pattern recognition and feature fusion formulation for vehicle Reidentification in intelligent transportation systems
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: R P Ramachandran, Stephen G RitchieAbstract:Vehicle Reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle Reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving Intelligent Transportation Systems (ITS) by providing more accurate data. This paper presents a new vehicle Reidentification algorithm that uses four different features, namely, (I) the inductive signature vector acquired from loop detectors, (2) vehicle velocity, (3) traversal time and (4) color information (based on images acquired from video cameras) to achieve high accuracy. A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 91 percent accuracy is obtained on real data collected from a parkway in California.
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use of vehicle signature analysis and lexicographic optimization for vehicle Reidentification on freeways
Transportation Research Part C-emerging Technologies, 1999Co-Authors: Carlos Sun, Stephen G Ritchie, Kevin Tsai, R JayakrishnanAbstract:Abstract The vehicle Reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle Reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle Reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.
R Jayakrishnan - One of the best experts on this subject based on the ideXlab platform.
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use of vehicle signature analysis and lexicographic optimization for vehicle Reidentification on freeways
Transportation Research Part C-emerging Technologies, 1999Co-Authors: Carlos Sun, Stephen G Ritchie, Kevin Tsai, R JayakrishnanAbstract:Abstract The vehicle Reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle Reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle Reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.
Alex Pentland - One of the best experts on this subject based on the ideXlab platform.
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response to comment on unique in the shopping mall on the reidentifiability of credit card metadata
Science, 2016Co-Authors: Yves-alexandre De Montjoye, Alex PentlandAbstract:Sanchez et al.’s textbook k-anonymization example does not prove, or even suggest, that location and other big-data data sets can be anonymized and of general use. The synthetic data set that they “successfully anonymize” bears no resemblance to modern high-dimensional data sets on which their methods fail. Moving forward, deidentification should not be considered a useful basis for policy.
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assessing data intrusion threats response
Science, 2015Co-Authors: Yves-alexandre De Montjoye, Alex PentlandAbstract:Barth-Jones et al. claim that our findings “reflect unrealistic data intrusion threats.” We strongly disagree and argue that Barth-Jones et al. 's Letter is instead a superb illustration of why deidentification is not “a useful basis for policy” ([ 1 ][1]). A simple and real example of our attack model is a bank sharing metadata for its 1.1 million customers in anonymized form with a third party for analysis. If the third party is able to obtain additional information—such as loyalty program data if the third party is a retailer—that data could be used to reidentify an individual and all the rest of his or her purchases. Barth-Jones et al. 's Letter exemplifies the intrinsic issue with deidentification. One can always, as Barth-Jones et al. have, artificially lower the estimated likelihood of Reidentification through the use of arbitrary and debatable assumptions. First, Barth-Jones et al. have consistently considered an intrusion to be a breach of privacy only if “all targeted customers” are reidentified ([ 2 ][2]). This is an unrealistic definition of breach of privacy. Second, Barth-Jones et al. assume that it is “very unlikely” for an attacker to be able to collect geolocalized information about an individual. At best, this is a striking underestimation of the current availability of identified data. Possible sources would include manually collected clues about an individual we know (e.g., receipts or branded shopping bags) ([ 3 ][3]); having access or collecting from public profiles people's check-ins at shops or restaurants on Yelp, Foursquare, or Facebook ([ 4 ][4]); or having access to a retailer's database or to a database of geolocalized information such as the one collected by smartphone applications ([ 5 ][5]), WiFicompanies, and virtually any carriers in the world. Third, Barth-Jones et al. assume that an attacker cannot know whether an individual is a client of a bank and is therefore in the data set. This is again an assumption that artificially lowers the estimated, and thus perceived, risks of Reidentification without changing at all the actual risk for people in the release data set. Fourth, the fact that an individual might occasionally pay cash only means that an attacker would need a few more points. Estimated probabilities of Reidentification are not a useful basis for policy, and we stand by our comment that “the open sharing of raw [deidentified metadata] data sets is not the future” ([ 6 ][6]). 1. [↵][7] President's Council of Advisors on Science and Technology, Big Data and Privacy: A Technological Perspective (PCAST, Washington, DC, 2014), pp. 38–39. 2. [↵][8] D. C. Barth-Jones, “Press and Reporting Considerations for Recent Re-Identification Demonstration Attacks: Part 2” ( ). 3. [↵][9] 1. L. Sweeney , Int. J. Uncertainty, Fuzziness Knowledge-Based Syst. 10.05, 557 (2002). [OpenUrl][10] 4. [↵][11] Wallaby, “Is anonymous financial data anonymous?” ([www.walla.by/blog/110651700144/is-anonymous-financial-data-anonymous][12]). 5. [↵][13] CNIL, “Mobilitics, season 2: Smartphones and their apps under the microscope” ([www.cnil.fr/english/news-and-events/news/article/mobilitics-season-2-smartphones-and-their-apps-under-the-microscope/][14]). 6. [↵][15] 1. J. Bohannon , Science 347, 468 (2015). [OpenUrl][16][Abstract/FREE Full Text][17] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #xref-ref-1-1 "View reference 1 in text" [8]: #xref-ref-2-1 "View reference 2 in text" [9]: #xref-ref-3-1 "View reference 3 in text" [10]: {openurl}?query=rft.jtitle%253DInt.%2BJ.%2BUncertainty%252C%2BFuzziness%2BKnowledge-Based%2BSyst.%26rft.volume%253D1005%26rft.spage%253D557%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [11]: #xref-ref-4-1 "View reference 4 in text" [12]: http://www.walla.by/blog/110651700144/is-anonymous-financial-data-anonymous [13]: #xref-ref-5-1 "View reference 5 in text" [14]: http://www.cnil.fr/english/news-and-events/news/article/mobilitics-season-2-smartphones-and-their-apps-under-the-microscope/ [15]: #xref-ref-6-1 "View reference 6 in text" [16]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.issn%253D0036-8075%26rft.aulast%253DBohannon%26rft.auinit1%253DJ.%26rft.volume%253D347%26rft.issue%253D6221%26rft.spage%253D468%26rft.epage%253D468%26rft.atitle%253DCredit%2Bcard%2Bstudy%2Bblows%2Bholes%2Bin%2Banonymity%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.347.6221.468%26rft_id%253Dinfo%253Apmid%252F25635068%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNDcvNjIyMS80NjgiO3M6NDoiYXRvbSI7czoyNDoiL3NjaS8zNDgvNjIzMS8xOTUuMS5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=
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unique in the shopping mall on the reidentifiability of credit card metadata
Science, 2015Co-Authors: Yves-alexandre De Montjoye, Laura Radaelli, Vivek Singh, Alex PentlandAbstract:Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of Reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.
Carlos Sun - One of the best experts on this subject based on the ideXlab platform.
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use of vehicle signature analysis and lexicographic optimization for vehicle Reidentification on freeways
Transportation Research Part C-emerging Technologies, 1999Co-Authors: Carlos Sun, Stephen G Ritchie, Kevin Tsai, R JayakrishnanAbstract:Abstract The vehicle Reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle Reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle Reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.
Karl Petty - One of the best experts on this subject based on the ideXlab platform.
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software based vehicle Reidentification with existing loop infrastructure
Transportation Research Record, 2014Co-Authors: Jaimyoung Kwon, Karl PettyAbstract:This paper describes the development of a hybrid Reidentification algorithm to estimate travel times entirely on the basis of data that are readily available from single loop detectors (60-Hz vehicle occupancy data) without the need for additional hardware in the field. The key concept is data fusion, which combines crude and potentially inaccurate spot-point estimates with a software-based signature-matching algorithm. The method was applied to real data from an urban freeway site with four locations and six segments, with Bluetooth-matched travel time as the ground truth. The method performed satisfactorily, with a relative error of 9% for a segment 1.54 mi long, among others. It removed bias and improved the baseline spot-speed method significantly. The approach requires minimum calibration with no additional hardware. The method, therefore, proves suitable for widespread deployment and provides a clear path for agencies to leverage their existing loop and controller infrastructure for accurate travel ...
