Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Zhuravlev Pavel"'
Autor:
Budi Hendrik Setia, Elveny Marischa, Zhuravlev Pavel, Jalil Abduladheem Turki, Al-Janabi Samaher, Alkaim Ayad F., Saleh Marwan Mahmood, Shichiyakh Rustem Adamovich, Sutarto
Publikováno v:
Foundations of Computing and Decision Sciences, Vol 47, Iss 2, Pp 111-125 (2022)
The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing
Externí odkaz:
https://doaj.org/article/d77549c075414f3792183cc9c2c04ba7
Autor:
Sukhina Nadezhda, Lebedeva Ekaterina, Zhuravlev Pavel, Chernykh Inna, Kotova Xenia, Hajiyev Hafis A.
Publikováno v:
International Review, Vol 2021, Iss 3-4, Pp 179-187 (2021)
The article discusses financial management methods and ways to optimize costs. Management of the cost of production of enterprises is a systematic process of formation of costs for the production of all products and the cost of individual products, c
Externí odkaz:
https://doaj.org/article/53f8fdc5955d4e3aa02b1934ebe20e30
Autor:
Cumings-Menon, Ryan, Ashmead, Robert, Kifer, Daniel, Leclerc, Philip, Spence, Matthew, Zhuravlev, Pavel, Abowd, John M.
In "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Abowd et al. (2022) describe the concepts and methods used by the Disclosure Avoidance System (DAS) to produce formally private output in support of the 2020 Census data product rele
Externí odkaz:
http://arxiv.org/abs/2312.10863
Autor:
Jarmin, Ron S., Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Goldschlag, Nathan, Hawes, Michael B., Keller, Sallie Ann, Kifer, Daniel, Leclerc, Philip, Reiter, Jerome P., Rodríguez, Rolando A., Schmutte, Ian, Velkoff, Victoria A., Zhuravlev, Pavel
Publikováno v:
PNAS, October 13, 2023, Vol. 120, No. 43
The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published dat
Externí odkaz:
http://arxiv.org/abs/2310.09398
Publikováno v:
E3S Web of Conferences, Vol 97, p 03038 (2019)
As noted by a number of experts in the field of the quality of construction of capital construction objects, at present there are no industry-specific methods for assessing the quality of construction and installation works in construction. The quali
Externí odkaz:
https://doaj.org/article/21b1de68202d49b1a48e7d21be558b1a
Publikováno v:
MATEC Web of Conferences, Vol 251, p 05039 (2018)
Solving the problems of quality assurance is an integral element of the development strategy of any industry, including the investment and construction sector. Ensuring product quality is not only a competitive advantage, but also helps to reduce unf
Externí odkaz:
https://doaj.org/article/2338c02b7ce94f3da55e668aa77d3a42
Autor:
Kifer, Daniel, Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Leclerc, Philip, Machanavajjhala, Ashwin, Sexton, William, Zhuravlev, Pavel
The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, R\'enyi, zero-concentrated, and $f$ differential privacy. We interpret
Externí odkaz:
http://arxiv.org/abs/2209.03310
Autor:
Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Garfinkel, Simson, Heineck, Micah, Heiss, Christine, Johns, Robert, Kifer, Daniel, Leclerc, Philip, Machanavajjhala, Ashwin, Moran, Brett, Sexton, William, Spence, Matthew, Zhuravlev, Pavel
The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The a
Externí odkaz:
http://arxiv.org/abs/2204.08986
Autor:
Cumings-Menon, Ryan, Abowd, John M., Ashmead, Robert, Kifer, Daniel, Leclerc, Philip, Ocker, Jeffrey, Ratcliffe, Michael, Zhuravlev, Pavel
The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geographic units, which is known as the geographic spine. After post-proc
Externí odkaz:
http://arxiv.org/abs/2203.16654
Autor:
Abowd, John, Ashmead, Robert, Cumings-Menon, Ryan, Garfinkel, Simson, Kifer, Daniel, Leclerc, Philip, Sexton, William, Simpson, Ashley, Task, Christine, Zhuravlev, Pavel
Privacy-protected microdata are often the desired output of a differentially private algorithm since microdata is familiar and convenient for downstream users. However, there is a statistical price for this kind of convenience. We show that an uncert
Externí odkaz:
http://arxiv.org/abs/2110.13239