Differentially Private MIMO Filtering for Event Streams von Le Ny, Jerome, Mohammady, Meisam

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A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing von Xie, Shangyu, Mohammady, Meisam, Wang, Han, Wang, Lingyu, Vaidya, Jaideep, Hong, Yuan

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Optimally Mitigating Backdoor Attacks in Federated Learning von Walter, Kane, Mohammady, Meisam, Nepal, Surya, Kanhere, Salil S.

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SegGuard: Segmentation-Based Anonymization of Network Data in Clouds for Privacy-Preserving Security Auditing von Oqaily, Momen, Jarraya, Yosr, Mohammady, Meisam, Majumdar, Suryadipta, Pourzandi, Makan, Wang, Lingyu, Debbabi, Mourad

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VTDP: Privately Sanitizing Fine-Grained Vehicle Trajectory Data With Boosted Utility von Liu, Bingyu, Xie, Shangyu, Wang, Han, Hong, Yuan, Ban, Xuegang, Mohammady, Meisam

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DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming von Feng, Shuya, Mohammady, Meisam, Wang, Han, Li, Xiaochen, Qin, Zhan, Hong, Yuan

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Randomized Privacy Budget Differential Privacy von Mohammady, Meisam

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A Multi-view Approach to Preserve Privacy and Utility in Network Trace Anonymization von Mohammady, Meisam, Oqaily, Momen, Wang, Lingyu, Hong, Yuan, Louafi, Habib, Pourzandi, Makan, Debbabi, Mourad

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Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis von Mohammady, Meisam, Arablouei, Reza

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Randomized Privacy Budget Differential Privacy von Mohammady, Meisam

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Proactivizer: Transforming Existing Verification Tools into Efficient Solutions for Runtime Security Enforcement von Majumdar, Suryadipta, Tabiban, Azadeh, Mohammady, Meisam, Oqaily, Alaa, Jarraya, Yosr, Pourzandi, Makan, Wang, Lingyu, Debbabi, Mourad

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Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence von Feng, Shuya, Mohammady, Meisam, Hong, Hanbin, Yan, Shenao, Kundu, Ashish, Wang, Binghui, Hong, Yuan

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DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming von Feng, Shuya, Mohammady, Meisam, Wang, Han, Li, Xiaochen, Qin, Zhan, Hong, Yuan

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OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy von 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

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DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning von Mohammady, Meisam, Wang, Han, Wang, Lingyu, Zhang, Mengyuan, Jarraya, Yosr, Majumdar, Suryadipta, Pourzandi, Makan, Debbabi, Mourad, Hong, Yuan

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R$^2$DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions von Mohammady, Meisam, Xie, Shangyu, Hong, Yuan, Zhang, Mengyuan, Wang, Lingyu, Pourzandi, Makan, Debbabi, Mourad

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Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence von Feng, Shuya, Mohammady, Meisam, Hong, Hanbin, Shenao Yan, Kundu, Ashish, Wang, Binghui, Yuan, Hong

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DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming von Feng, Shuya, Mohammady, Meisam, Wang, Han, Li, Xiaochen, Zhan Qin, Yuan, Hong

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Partition-based prefix preserving anonymization approach for network traces containing IP addresses von Jarraya, Yosr, Mohammady, Meisam, Wang, Lingyu, Pourzandi, Makan, Debbabi, Mourad

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PROTECTING AN INPUT DATASET AGAINST LINKING WITH FURTHER DATASETS von OPPERMANN, Ian, PAL, Arindam, LIU, Dongxi, RUJ, Sushmita, MOHAMMADY, Meisam, NEPAL, Surya, JANG, Seung, CEMTEPE, Sevit, ARACHCHIGE, Chamikara, MUSOTTO, Roberto

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