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pro vyhledávání: '"Taft, Nina"'
Autor:
Akgul, Omer, Peddinti, Sai Teja, Taft, Nina, Mazurek, Michelle L., Harkous, Hamza, Srivastava, Animesh, Seguin, Benoit
We present an analysis of 12 million instances of privacy-relevant reviews publicly visible on the Google Play Store that span a 10 year period. By leveraging state of the art NLP techniques, we examine what users have been writing about privacy alon
Externí odkaz:
http://arxiv.org/abs/2403.02292
Autor:
Salamatian, Salman, Zhang, Amy, Calmon, Flavio du Pin, Bhamidipati, Sandilya, Fawaz, Nadia, Kveton, Branislav, Oliveira, Pedro, Taft, Nina
We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, in the hope of getting some utility. Our approach relies on a general statistical inference framewo
Externí odkaz:
http://arxiv.org/abs/1408.3698
Autor:
Ioannidis, Stratis, Montanari, Andrea, Weinsberg, Udi, Bhagat, Smriti, Fawaz, Nadia, Taft, Nina
Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender)
Externí odkaz:
http://arxiv.org/abs/1403.8084
Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of
Externí odkaz:
http://arxiv.org/abs/1311.6802
In this work we focus on modeling a little studied type of traffic, namely the network traffic generated from endhosts. We introduce a parsimonious parametric model of the marginal distribution for connection arrivals. We employ mixture models based
Externí odkaz:
http://arxiv.org/abs/1212.2744
Autor:
Weinsberg, Udi, Balachandran, Athula, Taft, Nina, Iannaccone, Gianluca, Sekar, Vyas, Seshan, Srinivasan
During a disaster scenario, situational awareness information, such as location, physical status and images of the surrounding area, is essential for minimizing loss of life, injury, and property damage. Today's handhelds make it easy for people to g
Externí odkaz:
http://arxiv.org/abs/1206.1815
In monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses - computing predicate sums - with privacy. Formally, we study the problem of estimat
Externí odkaz:
http://arxiv.org/abs/1108.6123
Several recent studies in privacy-preserving learning have considered the trade-off between utility or risk and the level of differential privacy guaranteed by mechanisms for statistical query processing. In this paper we study this trade-off in priv
Externí odkaz:
http://arxiv.org/abs/0911.5708
Akademický článek
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Publikováno v:
Liang, Gang; Taft, Nina; & Yu, Bin. (2005). A Fast Lightweight Approach to Orgin-Destination IP Traffic Estimation Using Partial Measurements. Department of Statistics, UCI. UC Irvine: Department of Statistics, UCI. Retrieved from: http://www.escholarship.org/uc/item/7q18k8v9
In this paper, we propose an approach to estimating traffic matrices that incorporates lightweight Origin- Destination (OD) flow measurements coupled with a computationally lightweight algorithm for producing the OD estimates. There are two key ingre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::c982e9680d6a98bb42ee7f3b5a3bdf81
http://www.escholarship.org/uc/item/7q18k8v9
http://www.escholarship.org/uc/item/7q18k8v9