Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Mohammady, Meisam"'
Autor:
Feng, Shuya, Mohammady, Meisam, Hong, Hanbin, Yan, Shenao, Kundu, Ashish, Wang, Binghui, Hong, Yuan
Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tradeoff between privacy and accuracy remains a critical challenge. To ou
Externí odkaz:
http://arxiv.org/abs/2407.14710
Streaming data, crucial for applications like crowdsourcing analytics, behavior studies, and real-time monitoring, faces significant privacy risks due to the large and diverse data linked to individuals. In particular, recent efforts to release data
Externí odkaz:
http://arxiv.org/abs/2312.04738
Autor:
Chamikara, M. A. P., Jang, Seung Ick, Oppermann, Ian, Liu, Dongxi, Roberto, Musotto, Ruj, Sushmita, Pal, Arindam, Mohammady, Meisam, Camtepe, Seyit, Young, Sylvia, Dorrian, Chris, David, Nasir
Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus, ensuring privacy-preserving data sharing is crucial. Differential pr
Externí odkaz:
http://arxiv.org/abs/2306.03379
Autor:
Mohammady, Meisam, Arablouei, Reza
We estimate vehicular traffic states from multimodal data collected by single-loop detectors while preserving the privacy of the individual vehicles contributing to the data. To this end, we propose a novel hybrid differential privacy (DP) approach t
Externí odkaz:
http://arxiv.org/abs/2302.09783
Autor:
Mohammady, Meisam
While pursuing better utility by discovering knowledge from the data, individual's privacy may be compromised during an analysis. To that end, differential privacy has been widely recognized as the state-of-the-art privacy notion. By requiring the pr
Externí odkaz:
http://arxiv.org/abs/2209.01468
Autor:
Mohammady, Meisam, Wang, Han, Wang, Lingyu, Zhang, Mengyuan, Jarraya, Yosr, Majumdar, Suryadipta, Pourzandi, Makan, Debbabi, Mourad, Hong, Yuan
Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e.g., in lightweight IoT devices), facilitate collaborative analysis (e.g., under distributed or multi-party scenarios), and benefit from lower cos
Externí odkaz:
http://arxiv.org/abs/2206.13046
Autor:
Mohammady, Meisam, Xie, Shangyu, Hong, Yuan, Zhang, Mengyuan, Wang, Lingyu, Pourzandi, Makan, Debbabi, Mourad
Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly differ, a key challenge is to find the optimal randomization mechanis
Externí odkaz:
http://arxiv.org/abs/2009.09451
Autor:
Mohammady, Meisam, Wang, Lingyu, Hong, Yuan, Louafi, Habib, Pourzandi, Makan, Debbabi, Mourad
As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive
Externí odkaz:
http://arxiv.org/abs/1810.10464
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Akademický článek
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