Nathalie Baracaldo

105 papers A* 10A 4B 7C 5Misc 2Journal 50Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Entropy-Aware On-Policy Distillation of Language Models. CoRR Woogyeol Jin, Taywon Min, Yongjin Yang, Swanand Ravindra Kadhe, Yi Zhou, Dennis Wei, Nathalie Baracaldo, Kimin Lee
2026	J	`jnl`	The Unlearning Mirage: A Dynamic Framework for Evaluating LLM Unlearning. CoRR Raj Sanjay Shah, Jing Huang, Keerthiram Murugesan, Nathalie Baracaldo, Diyi Yang
2025	J	`jnl`	Beyond SFT: Reinforcement Learning for Safer Large Reasoning Models with Better Reasoning Ability. CoRR Jinghan Jia, Nathalie Baracaldo, Sijia Liu
2025	J	`jnl`	EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation. CoRR Jinghan Jia, Hadi Reisizadeh, Chongyu Fan, Nathalie Baracaldo, Mingyi Hong, Sijia Liu
2025	J	`jnl`	Evaluating the Dynamics of Membership Privacy in Deep Learning. CoRR Yuetian Chen, Zhiqi Wang, Nathalie Baracaldo, Swanand Ravindra Kadhe, Lei Yu
2025	—	`conf`	FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. KDD (2) Carl Yang, Guancheng Wan, Zhuangdi Zhu, Zheng Xu, Junyuan Hong, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
2025	J	`jnl`	In-Context Probing for Membership Inference in Fine-Tuned Language Models. CoRR Zhexi Lu, Hongliang Chi, Nathalie Baracaldo, Swanand Ravindra Kadhe, Yuseok Jeon, Lei Yu
2025	A*	`conf`	Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning. ICML Changsheng Wang, Yihua Zhang, Jinghan Jia, Parikshit Ram, Dennis Wei, Yuguang Yao, Soumyadeep Pal, Nathalie Baracaldo, Sijia Liu
2025	J	`jnl`	Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning. CoRR Changsheng Wang, Yihua Zhang, Jinghan Jia, Parikshit Ram, Dennis Wei, Yuguang Yao, Soumyadeep Pal, Nathalie Baracaldo, Sijia Liu
2025	A*	`conf`	MAP: Multi-Human-Value Alignment Palette. ICLR Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
2025	A*	`conf`	Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble. CCS Zhiqi Wang, Chengyu Zhang, Yuetian Chen, Nathalie Baracaldo, Swanand Ravindra Kadhe, Lei Yu
2025	J	`jnl`	Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble. CoRR Zhiqi Wang, Chengyu Zhang, Yuetian Chen, Nathalie Baracaldo, Swanand Kadhe, Lei Yu
2025	J	`jnl`	Mitigating Modality Imbalance in Multi-modal Learning via Multi-objective Optimization. CoRR Heshan Devaka Fernando, Parikshit Ram, Yi Zhou, Soham Dan, Horst Samulowitz, Nathalie Baracaldo, Tianyi Chen
2025	A*	`conf`	Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills. EMNLP Changsheng Wang, Chongyu Fan, Yihua Zhang, Jinghan Jia, Dennis Wei, Parikshit Ram, Nathalie Baracaldo, Sijia Liu
2025	J	`jnl`	Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills. CoRR Changsheng Wang, Chongyu Fan, Yihua Zhang, Jinghan Jia, Dennis Wei, Parikshit Ram, Nathalie Baracaldo, Sijia Liu
2025	J	`jnl`	Rethinking machine unlearning for large language models. Nat. Mac. Intell. Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
2025	A*	`conf`	Towards a Re-evaluation of Data Forging Attacks in Practice. USENIX Security Symposium Mohamed Suliman, Anisa Halimi, Swanand Ravindra Kadhe, Nathalie Baracaldo, Douglas J. Leith
2024	—	`conf`	Byzantine-Resilient Bilevel Federated Learning. SAM Momin Abbas, Yi Zhou, Nathalie Baracaldo, Horst Samulowitz, Parikshit Ram, Theodoros Salonidis
2024	A	`conf`	Enhancing In-context Learning via Linear Probe Calibration. AISTATS Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
2024	J	`jnl`	Enhancing In-context Learning via Linear Probe Calibration. CoRR Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
2024	A*	`conf`	FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. KDD Junyuan Hong, Carl Yang, Zhuangdi Zhu, Zheng Xu, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
2024	—	`conf`	Is Federated Learning Still Alive in the Foundation Model Era? AAAI Spring Symposia Nathalie Baracaldo
2024	J	`jnl`	MAP: Multi-Human-Value Alignment Palette. CoRR Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
2024	J	`jnl`	Mitigating Forgetting in LLM Supervised Fine-Tuning and Preference Learning. CoRR Heshan Devaka Fernando, Han Shen, Parikshit Ram, Yi Zhou, Horst Samulowitz, Nathalie Baracaldo, Tianyi Chen
2024	J	`jnl`	Rethinking Machine Unlearning for Large Language Models. CoRR Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
2024	J	`jnl`	Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs. CoRR Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei, Nathalie Baracaldo, Inkit Padhi
2024	J	`jnl`	Towards a Re-evaluation of Data Forging Attacks in Practice. CoRR Mohamed Suliman, Anisa Halimi, Swanand Kadhe, Nathalie Baracaldo, Douglas J. Leith
2024	J	`jnl`	Turning Generative Models Degenerate: The Power of Data Poisoning Attacks. CoRR Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Farhan Ahmed, Ling Cai, Nathalie Baracaldo
2024	A*	`conf`	WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models. NeurIPS Jinghan Jia, Jiancheng Liu, Yihua Zhang, Parikshit Ram, Nathalie Baracaldo, Sijia Liu
2024	J	`jnl`	WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models. CoRR Jinghan Jia, Jiancheng Liu, Yihua Zhang, Parikshit Ram, Nathalie Baracaldo, Sijia Liu
2023	—	`conf`	Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models. SP (Workshops) Nathalie Baracaldo, Farhan Ahmed, Kevin Eykholt, Yi Zhou, Shriti Priya, Taesung Lee, Swanand Kadhe, Mike Tan, Sridevi Polavaram, Sterling Suggs, Yuyang Gao, David Slater
2023	J	`jnl`	FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs. CoRR Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo
2023	J	`jnl`	Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks. CoRR Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Ling Cai, Nathalie Baracaldo
2023	B	`conf`	HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. CCGrid Syed Zawad, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Feng Yan
2023	A*	`conf`	LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. ICML Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
2023	J	`jnl`	LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. CoRR Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
2023	J	`jnl`	Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. CoRR Swanand Ravindra Kadhe, Heiko Ludwig, Nathalie Baracaldo, Alan King, Yi Zhou, Keith Houck, Ambrish Rawat, Mark Purcell, Naoise Holohan, Mikio Takeuchi, Ryo Kawahara, Nir Drucker, Hayim Shaul, Eyal Kushnir, Omri Soceanu
2023	—	`ed.`	Proceedings of the 4th International Workshop on Distributed Machine Learning, DistributedML 2023, Paris, France, 8 December 2023 DistributedML@CoNEXT Stefanos Laskaridis, Alexey Tumanov, Nathalie Baracaldo, Dimitrios Vytiniotis
2023	A*	`conf`	Single-shot General Hyper-parameter Optimization for Federated Learning. ICLR Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
2022	J	`jnl`	A Distributed and Elastic Aggregation Service for Scalable Federated Learning Systems. CoRR Ahmad Khan, Yuze Li, Ali Anwar, Yue Cheng, Thang Hoang, Nathalie Baracaldo, Ali Raza Butt
2022	B	`conf`	DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. CLOUD Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig
2022	J	`jnl`	DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. CoRR Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig
2022	—	`ch.`	Dealing with Byzantine Threats to Neural Networks. Federated Learning Yi Zhou, Nathalie Baracaldo, Ali Anwar, Kamala Varma
2022	—	`book`	Federated Learning - A Comprehensive Overview of Methods and Applications Federated Learning Heiko Ludwig, Nathalie Baracaldo
2022	—	`ch.`	Federated Learning and Fairness. Federated Learning Annie Abay, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig
2022	—	`ch.`	Federated Learning for Collaborative Financial Crimes Detection. Federated Learning Toyotaro Suzumura, Yi Zhou, Ryo Kawahara, Nathalie Baracaldo, Heiko Ludwig
2022	J	`jnl`	Federated Unlearning: How to Efficiently Erase a Client in FL? CoRR Anisa Halimi, Swanand Kadhe, Ambrish Rawat, Nathalie Baracaldo
2022	J	`jnl`	Federated XGBoost on Sample-Wise Non-IID Data. CoRR Katelinh Jones, Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo
2022	B	`conf`	Heterogeneity-Aware Adaptive Federated Learning Scheduling. IEEE Big Data Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Feng Yan, Ali Raza Butt
2022	—	`ch.`	Introduction to Federated Learning. Federated Learning Heiko Ludwig, Nathalie Baracaldo
2022	C	`conf`	Keynote Talk - Federated Learning: The Hype, State-of-the-Art and Open Challenges. SACMAT Nathalie Baracaldo
2022	J	`jnl`	Machine Learning Security and Privacy. IEEE Secur. Priv. Nathalie Baracaldo, Alina Oprea
2022	—	`ch.`	Privacy-Preserving Vertical Federated Learning. Federated Learning Runhua Xu, Nathalie Baracaldo, Yi Zhou, Annie Abay, Ali Anwar
2022	—	`ch.`	Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose? Federated Learning Nathalie Baracaldo, Runhua Xu
2022	J	`jnl`	Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. CoRR Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
2022	B	`conf`	TIFF: Tokenized Incentive for Federated Learning. CLOUD Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Feng Yan, Ali Raza Butt
2022	A	`conf`	The 1st International Workshop on Federated Learning with Graph Data (FedGraph). CIKM Carl Yang, Xiaoxiao Li, Nathalie Baracaldo, Neil Shah, Chaoyang He, Lingjuan Lyu, Lichao Sun, Salman Avestimehr
2022	J	`jnl`	Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach. CoRR Nathalie Baracaldo, Ali Anwar, Mark Purcell, Ambrish Rawat, Mathieu Sinn, Bashar Altakrouri, Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schöpp, Matthias Buchinger
2022	—	`ch.`	Tree-Based Models for Federated Learning Systems. Federated Learning Yuya Jeremy Ong, Nathalie Baracaldo, Yi Zhou
2021	—	`conf`	Accountable Federated Machine Learning in Government: Engineering and Management Insights. ePart Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schöpp, Matthias Buchinger, Nathalie Baracaldo, Ali Anwar, Heiko Ludwig, Mathieu Sinn, Mark Purcell, Bashar Altakrouri
2021	—	`conf`	Conference Tutorial: Can federated learning solve our data privacy problems? State of the art and open challenges. TPS-ISA Nathalie Baracaldo
2021	A*	`conf`	Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. AAAI Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
2021	J	`jnl`	Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. CoRR Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
2021	J	`jnl`	FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. CoRR Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
2021	—	`conf`	FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. AISec@CCS Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig
2021	J	`jnl`	FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. CoRR Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig
2021	B	`conf`	LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. CLOUD Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar
2021	J	`jnl`	LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. CoRR Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar
2021	J	`jnl`	Privacy-Preserving Machine Learning: Methods, Challenges and Directions. CoRR Runhua Xu, Nathalie Baracaldo, James Joshi
2021	A	`conf`	The Design and Development of a Game to Study Backdoor Poisoning Attacks: The Backdoor Game. IUI Zahra Ashktorab, Casey Dugan, James Johnson, Aabhas Sharma, Dustin Ramsey Torres, Ingrid Lange, Benjamin Hoover, Heiko Ludwig, Bryant Chen, Nathalie Baracaldo, Werner Geyer, Qian Pan
2020	J	`jnl`	Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning. CoRR Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig
2020	J	`jnl`	IBM Federated Learning: an Enterprise Framework White Paper V0.1. CoRR Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Jeremy Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Ngoc Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Witherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay
2020	J	`jnl`	Mitigating Bias in Federated Learning. CoRR Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, Heiko Ludwig
2020	A	`conf`	TiFL: A Tier-based Federated Learning System. HPDC Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng
2020	J	`jnl`	TiFL: A Tier-based Federated Learning System. CoRR Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng
2020	J	`jnl`	Towards Privacy Preservation and Data Protection in Information System Design. An introduction to the special issue. Enterp. Model. Inf. Syst. Archit. Agnes Koschmider, Judith Michael, Nathalie Baracaldo
2019	J	`jnl`	A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). Inform. Spektrum Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
2019	—	`conf`	A Hybrid Approach to Privacy-Preserving Federated Learning. AISec@CCS Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
2019	—	`conf`	Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. SafeAI@AAAI Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian M. Molloy, Biplav Srivastava
2019	J	`jnl`	G-SIR: An Insider Attack Resilient Geo-Social Access Control Framework. IEEE Trans. Dependable Secur. Comput. Nathalie Baracaldo, Balaji Palanisamy, James Joshi
2019	—	`conf`	HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. AISec@CCS Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig
2019	J	`jnl`	HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. CoRR Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig
2019	J	`jnl`	Privacy-Preserving Process Mining - Differential Privacy for Event Logs. Bus. Inf. Syst. Eng. Felix Mannhardt, Agnes Koschmider, Nathalie Baracaldo, Matthias Weidlich, Judith Michael
2019	J	`jnl`	Privacy-preserving Process Mining: Differential - Privacy for Event Logs (Extended Abstract). Inform. Spektrum Felix Mannhardt, Agnes Koschmider, Nathalie Baracaldo, Matthias Weidlich, Judith Michael
2019	—	`conf`	Towards Taming the Resource and Data Heterogeneity in Federated Learning. OpML Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, Yue Cheng
2019	J	`jnl`	User Centered and Privacy-Driven Process Mining System Design - (Extended Abstract). Inform. Spektrum Judith Michael, Agnes Koschmider, Felix Mannhardt, Nathalie Baracaldo, Bernhard Rumpe
2019	—	`conf`	User-Centered and Privacy-Driven Process Mining System Design for IoT. CAiSE Forum Judith Michael, Agnes Koschmider, Felix Mannhardt, Nathalie Baracaldo, Bernhard Rumpe
2019	—	`conf`	Using BPM Technology to Deploy and Manage Distributed Analytics in Collaborative IoT-Driven Business Scenarios. IOT Tim D'Hondt, Anna Wilbik, Paul Grefen, Heiko Ludwig, Nathalie Baracaldo, Ali Anwar
2018	J	`jnl`	A Hybrid Approach to Privacy-Preserving Federated Learning. CoRR Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang
2018	C	`conf`	Complex Collaborative Physical Process Management: A Position on the Trinity of BPM, IoT and DA. PRO-VE Paul Grefen, Heiko Ludwig, Samir Tata, Remco M. Dijkman, Nathalie Baracaldo, Anna Wilbik, Tim D'Hondt
2018	J	`jnl`	Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. CoRR Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian M. Molloy, Biplav Srivastava
2018	—	`conf`	Detecting Poisoning Attacks on Machine Learning in IoT Environments. ICIOT Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Jaehoon Amir Safavi, Rui Zhang
2017	—	`conf`	Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach. AISec@CCS Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Jaehoon Amir Safavi
2017	J	`jnl`	rSLA: An Approach for Managing Service Level Agreements in Cloud Environments. Int. J. Cooperative Inf. Syst. Mohamed Mohamed, Obinna Anya, Samir Tata, NagaPramod Mandagere, Nathalie Baracaldo, Heiko Ludwig
2016	—	`conf`	Data Provenance Model for Internet of Things (IoT) Systems. ICSOC Workshops Habeeb Olufowobi, Robert Engel, Nathalie Baracaldo, Luis Angel D. Bathen, Samir Tata, Heiko Ludwig
2016	C	`conf`	Managing Service Quality at the Platform and Application Levels with rSLa. WETICE Samir Tata, Mohamed Mohamed, Obinna Anya, Takashi Sakairi, NagaPramod Mandagere, Heiko Ludwig, Nathalie Baracaldo
2016	—	`conf`	Securing Data Provenance in Internet of Things (IoT) Systems. ICSOC Workshops Nathalie Baracaldo, Luis Angel D. Bathen, Roqeeb O. Ozugha, Robert Engel, Samir Tata, Heiko Ludwig
2014	B	`conf`	Geo-Social-RBAC: A Location-Based Socially Aware Access Control Framework. NSS Nathalie Baracaldo, Balaji Palanisamy, James B. D. Joshi
2014	Misc	`conf`	IEEE IRI 2014 invited industry talks (I): Managing shared information in multi-tenant service provider applications. IRI Heiko Ludwig, Nathalie Baracaldo, Nish Parikh, Tanvir Ahmed, Rajesh Subramanyan
2014	—	`conf`	Reconciling End-to-End Confidentiality and Data Reduction In Cloud Storage. CCSW Nathalie Baracaldo, Elli Androulaki, Joseph S. Glider, Alessandro Sorniotti
2013	J	`jnl`	An adaptive risk management and access control framework to mitigate insider threats. Comput. Secur. Nathalie Baracaldo, James Joshi
2013	C	`conf`	Beyond accountability: using obligations to reduce risk exposure and deter insider attacks. SACMAT Nathalie Baracaldo, James Joshi
2012	C	`conf`	A trust-and-risk aware RBAC framework: tackling insider threat. SACMAT Nathalie Baracaldo, James Joshi
2011	B	`conf`	A secure, constraint-aware role-based access control interoperation framework. NSS Nathalie Baracaldo, Amirreza Masoumzadeh, James Joshi
2011	Misc	`conf`	Simulating the effect of privacy concerns in online social networks. IRI Nathalie Baracaldo, Claudia A. López, Mohd Anwar, Michael Lewis