Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Machanavajjhala, Ashwin"'
We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large entities. We propose a privacy formalism, per-record zero concentrated d
Externí odkaz:
http://arxiv.org/abs/2310.12827
Synthetic data generation methods, and in particular, private synthetic data generation methods, are gaining popularity as a means to make copies of sensitive databases that can be shared widely for research and data analysis. Some of the fundamental
Externí odkaz:
http://arxiv.org/abs/2309.08574
Autor:
Adeleye, Temilola, Berghel, Skye, Desfontaines, Damien, Hay, Michael, Johnson, Isaac, Lemoisson, Cléo, Machanavajjhala, Ashwin, Magerlein, Tom, Modena, Gabriele, Pujol, David, Simmons-Marengo, Daniel, Triedman, Hal
For almost 20 years, the Wikimedia Foundation has been publishing statistics about how many people visited each Wikipedia page on each day. This data helps Wikipedia editors determine where to focus their efforts to improve the online encyclopedia, a
Externí odkaz:
http://arxiv.org/abs/2308.16298
When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the syn
Externí odkaz:
http://arxiv.org/abs/2212.10310
Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflicting priorities that must share the same privacy loss budget. This moti
Externí odkaz:
http://arxiv.org/abs/2212.09884
Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
Autor:
Berghel, Skye, Bohannon, Philip, Desfontaines, Damien, Estes, Charles, Haney, Sam, Hartman, Luke, Hay, Michael, Machanavajjhala, Ashwin, Magerlein, Tom, Miklau, Gerome, Pai, Amritha, Sexton, William, Shrestha, Ruchit
In this short paper, we outline the design of Tumult Analytics, a Python framework for differential privacy used at institutions such as the U.S. Census Bureau, the Wikimedia Foundation, or the Internal Revenue Service.
Externí odkaz:
http://arxiv.org/abs/2212.04133
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
Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in the data. In today's era of data analysis, however, it poses new challe
Externí odkaz:
http://arxiv.org/abs/2209.01286
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
In this paper, we consider secure outsourced growing databases that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view, such that they can use only the materialized view to process qu
Externí odkaz:
http://arxiv.org/abs/2203.05084