Simulating the effect of privacy concerns in online social networks. - Nathalie Baracaldo; Claudia A. López; Mohd Anwar; Michael Lewis 001 (IRI, 2011)

A secure, constraint-aware role-based access control interoperation framework. - Nathalie Baracaldo; Amirreza Masoumzadeh 001; James Joshi (NSS, 2011)

A trust-and-risk aware RBAC framework: tackling insider threat. - Nathalie Baracaldo; James Joshi (SACMAT, 2012)

Beyond accountability: using obligations to reduce risk exposure and deter insider attacks. - Nathalie Baracaldo; James Joshi (SACMAT, 2013)

An adaptive risk management and access control framework to mitigate insider threats. - Nathalie Baracaldo; James Joshi (Comput. Secur., 2013)

Reconciling End-to-End Confidentiality and Data Reduction In Cloud Storage. - Nathalie Baracaldo; Elli Androulaki; Joseph S. Glider; Alessandro Sorniotti (CCSW, 2014)

IEEE IRI 2014 invited industry talks (I): Managing shared information in multi-tenant service provider applications. - Heiko Ludwig; Nathalie Baracaldo; Nish Parikh; Tanvir Ahmed 002; Rajesh Subramanyan (IRI, 2014)

Geo-Social-RBAC: A Location-Based Socially Aware Access Control Framework. - Nathalie Baracaldo; Balaji Palanisamy; James B. D. Joshi (NSS, 2014)

Securing Data Provenance in Internet of Things (IoT) Systems. - Nathalie Baracaldo; Luis Angel D. Bathen; Roqeeb O. Ozugha; Robert Engel; Samir Tata; Heiko Ludwig (ICSOC Workshops, 2016)

Data Provenance Model for Internet of Things (IoT) Systems. - Habeeb Olufowobi; Robert Engel; Nathalie Baracaldo; Luis Angel D. Bathen; Samir Tata; Heiko Ludwig (ICSOC Workshops, 2016)

Managing Service Quality at the Platform and Application Levels with rSLa. - Samir Tata; Mohamed Mohamed 001; Obinna Anya; Takashi Sakairi; NagaPramod Mandagere; Heiko Ludwig; Nathalie Baracaldo (WETICE, 2016)

Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach. - Nathalie Baracaldo; Bryant Chen; Heiko Ludwig; Jaehoon Amir Safavi (AISec@CCS, 2017)

rSLA: An Approach for Managing Service Level Agreements in Cloud Environments. - Mohamed Mohamed 001; Obinna Anya; Samir Tata; NagaPramod Mandagere; Nathalie Baracaldo; Heiko Ludwig (Int. J. Cooperative Inf. Syst., 2017)

Detecting Poisoning Attacks on Machine Learning in IoT Environments. - Nathalie Baracaldo; Bryant Chen; Heiko Ludwig; Jaehoon Amir Safavi; Rui Zhang 028 (ICIOT, 2018)

Complex Collaborative Physical Process Management: A Position on the Trinity of BPM, IoT and DA. - Paul Grefen; Heiko Ludwig; Samir Tata; Remco M. Dijkman; Nathalie Baracaldo; Anna Wilbik; Tim D'Hondt (PRO-VE, 2018)

A Hybrid Approach to Privacy-Preserving Federated Learning. - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028 (CoRR, 2018)

Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. - Bryant Chen; Wilka Carvalho; Nathalie Baracaldo; Heiko Ludwig; Benjamin Edwards; Taesung Lee; Ian M. Molloy; Biplav Srivastava (SafeAI@AAAI, 2019)

User-Centered and Privacy-Driven Process Mining System Design for IoT. - Judith Michael; Agnes Koschmider; Felix Mannhardt; Nathalie Baracaldo; Bernhard Rumpe (CAiSE Forum, 2019)

A Hybrid Approach to Privacy-Preserving Federated Learning. - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028; Yi Zhou 015 (AISec@CCS, 2019)

HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Ali Anwar 001; Heiko Ludwig (AISec@CCS, 2019)

Using BPM Technology to Deploy and Manage Distributed Analytics in Collaborative IoT-Driven Business Scenarios. - Tim D'Hondt; Anna Wilbik; Paul Grefen; Heiko Ludwig; Nathalie Baracaldo; Ali Anwar 001 (IOT, 2019)

Towards Taming the Resource and Data Heterogeneity in Federated Learning. - Zheng Chai; Hannan Fayyaz; Zeshan Fayyaz; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Heiko Ludwig; Yue Cheng 001 (OpML, 2019)

Privacy-Preserving Process Mining - Differential Privacy for Event Logs. - Felix Mannhardt; Agnes Koschmider; Nathalie Baracaldo; Matthias Weidlich 001; Judith Michael (Bus. Inf. Syst. Eng., 2019)

Privacy-preserving Process Mining: Differential - Privacy for Event Logs (Extended Abstract). - Felix Mannhardt; Agnes Koschmider; Nathalie Baracaldo; Matthias Weidlich 001; Judith Michael (Inform. Spektrum, 2019)

User Centered and Privacy-Driven Process Mining System Design - (Extended Abstract). - Judith Michael; Agnes Koschmider; Felix Mannhardt; Nathalie Baracaldo; Bernhard Rumpe (Inform. Spektrum, 2019)

A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028; Yi Zhou 015 (Inform. Spektrum, 2019)

G-SIR: An Insider Attack Resilient Geo-Social Access Control Framework. - Nathalie Baracaldo; Balaji Palanisamy; James Joshi (IEEE Trans. Dependable Secur. Comput., 2019)

TiFL: A Tier-based Federated Learning System. - Zheng Chai; Ahsan Ali; Syed Zawad; Stacey Truex; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Heiko Ludwig; Feng Yan 001; Yue Cheng 001 (HPDC, 2020)

IBM Federated Learning: an Enterprise Framework White Paper V0.1. - Heiko Ludwig; Nathalie Baracaldo; Gegi Thomas; Yi Zhou 015; Ali Anwar 001; Shashank Rajamoni; Yuya Jeremy Ong; Jayaram Radhakrishnan; Ashish Verma 001; Mathieu Sinn; Mark Purcell; Ambrish Rawat; et al. (CoRR, 2020)

Mitigating Bias in Federated Learning. - Annie Abay; Yi Zhou 015; Nathalie Baracaldo; Shashank Rajamoni; Ebube Chuba; Heiko Ludwig (CoRR, 2020)

Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning. - Yuya Jeremy Ong; Yi Zhou 015; Nathalie Baracaldo; Heiko Ludwig (CoRR, 2020)

Towards Privacy Preservation and Data Protection in Information System Design. An introduction to the special issue. - Agnes Koschmider; Judith Michael; Nathalie Baracaldo (Enterp. Model. Inf. Syst. Archit. Int. J. Concept. Model., 2020)

LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. - Kamala Varma; Yi Zhou 015; Nathalie Baracaldo; Ali Anwar 001 (CLOUD, 2021)

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. - Syed Zawad; Ahsan Ali; Pin-Yu Chen; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Yuan Tian 001; Feng Yan 001 (AAAI, 2021)

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Ali Anwar 001; James Joshi; Heiko Ludwig (AISec@CCS, 2021)

Accountable Federated Machine Learning in Government: Engineering and Management Insights. - Dian Balta; Mahdi Sellami; Peter Kuhn; Ulrich Schöpp; Matthias Buchinger; Nathalie Baracaldo; Ali Anwar 001; Heiko Ludwig; Mathieu Sinn; Mark Purcell; Bashar Altakrouri (ePart, 2021)

The Design and Development of a Game to Study Backdoor Poisoning Attacks: The Backdoor Game. - 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 (IUI, 2021)

Conference Tutorial: Can federated learning solve our data privacy problems? State of the art and open challenges. - Nathalie Baracaldo (TPS-ISA, 2021)

Privacy-Preserving Machine Learning: Methods, Challenges and Directions. - Runhua Xu; Nathalie Baracaldo; James Joshi (CoRR, 2021)

FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (CoRR, 2021)

Dealing with Byzantine Threats to Neural Networks. - Yi Zhou 015; Nathalie Baracaldo; Ali Anwar 001; Kamala Varma (Federated Learning, 2022)

Federated Learning and Fairness. - Annie Abay; Yi Zhou 015; Nathalie Baracaldo; Heiko Ludwig (Federated Learning, 2022)

Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose? - Nathalie Baracaldo; Runhua Xu (Federated Learning, 2022)

Introduction to Federated Learning. - Heiko Ludwig; Nathalie Baracaldo (Federated Learning, 2022)

Tree-Based Models for Federated Learning Systems. - Yuya Jeremy Ong; Nathalie Baracaldo; Yi Zhou 015 (Federated Learning, 2022)

Federated Learning for Collaborative Financial Crimes Detection. - Toyotaro Suzumura; Yi Zhou 015; Ryo Kawahara; Nathalie Baracaldo; Heiko Ludwig (Federated Learning, 2022)

Privacy-Preserving Vertical Federated Learning. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Annie Abay; Ali Anwar 001 (Federated Learning, 2022)

TIFF: Tokenized Incentive for Federated Learning. - Jingoo Han; Ahmad Faraz Khan 001; Syed Zawad; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Feng Yan 001; Ali Raza Butt (CLOUD, 2022)

DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. - Runhua Xu; Nathalie Baracaldo; Yi Zhou 015; Ali Anwar 001; Swanand Kadhe; Heiko Ludwig (CLOUD, 2022)

Heterogeneity-Aware Adaptive Federated Learning Scheduling. - Jingoo Han; Ahmad Faraz Khan 001; Syed Zawad; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Feng Yan 001; Ali Raza Butt (IEEE Big Data, 2022)

The 1st International Workshop on Federated Learning with Graph Data (FedGraph). - Carl Yang 001; Xiaoxiao Li; Nathalie Baracaldo; Neil Shah; Chaoyang He 001; Lingjuan Lyu; Lichao Sun 001; Salman Avestimehr (CIKM, 2022)

Keynote Talk - Federated Learning: The Hype, State-of-the-Art and Open Challenges. - Nathalie Baracaldo (SACMAT, 2022)

Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (CoRR, 2022)

Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach. - Nathalie Baracaldo; Ali Anwar 001; Mark Purcell; Ambrish Rawat; Mathieu Sinn; Bashar Altakrouri; Dian Balta; Mahdi Sellami; Peter Kuhn; Ulrich Schöpp; Matthias Buchinger (CoRR, 2022)

A Distributed and Elastic Aggregation Service for Scalable Federated Learning Systems. - Ahmad Khan 001; Yuze Li; Ali Anwar 001; Yue Cheng 001; Thang Hoang; Nathalie Baracaldo; Ali Raza Butt (CoRR, 2022)

Federated Unlearning: How to Efficiently Erase a Client in FL? - Anisa Halimi; Swanand Kadhe; Ambrish Rawat; Nathalie Baracaldo (CoRR, 2022)

Federated XGBoost on Sample-Wise Non-IID Data. - Katelinh Jones; Yuya Jeremy Ong; Yi Zhou 015; Nathalie Baracaldo (CoRR, 2022)

Machine Learning Security and Privacy. - Nathalie Baracaldo; Alina Oprea (IEEE Secur. Priv., 2022)

HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. - Syed Zawad; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Feng Yan 001 (CCGrid, 2023)

Single-shot General Hyper-parameter Optimization for Federated Learning. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (ICLR, 2023)

LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. - Timothy Castiglia; Yi Zhou 015; Shiqiang Wang 001; Swanand Kadhe; Nathalie Baracaldo; Stacy Patterson (ICML, 2023)

Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models. - Nathalie Baracaldo; Farhan Ahmed; Kevin Eykholt; Yi Zhou 015; Shriti Priya; Taesung Lee; Swanand Kadhe; Mike Tan; Sridevi Polavaram; Sterling Suggs; Yuyang Gao; David Slater (SP (Workshops), 2023)

Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. - Swanand Ravindra Kadhe; Heiko Ludwig; Nathalie Baracaldo; Alan King; Yi Zhou 015; Keith Houck; Ambrish Rawat; Mark Purcell; Naoise Holohan; Mikio Takeuchi; Ryo Kawahara; Nir Drucker; et al. (CoRR, 2023)

Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks. - Shuli Jiang; Swanand Ravindra Kadhe; Yi Zhou 015; Ling Cai; Nathalie Baracaldo (CoRR, 2023)

FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs. - Swanand Ravindra Kadhe; Anisa Halimi; Ambrish Rawat; Nathalie Baracaldo (CoRR, 2023)

Is Federated Learning Still Alive in the Foundation Model Era? - Nathalie Baracaldo (AAAI Spring Symposia, 2024)

Enhancing In-context Learning via Linear Probe Calibration. - Momin Abbas; Yi Zhou 015; Parikshit Ram; Nathalie Baracaldo; Horst Samulowitz; Theodoros Salonidis; Tianyi Chen (AISTATS, 2024)

Byzantine-Resilient Bilevel Federated Learning. - Momin Abbas; Yi Zhou 015; Nathalie Baracaldo; Horst Samulowitz; Parikshit Ram; Theodoros Salonidis (SAM, 2024)

FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. - Junyuan Hong; Carl Yang 001; Zhuangdi Zhu; Zheng Xu 002; Nathalie Baracaldo; Neil Shah; Salman Avestimehr; Jiayu Zhou (KDD, 2024)

WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models. - Jinghan Jia; Jiancheng Liu; Yihua Zhang; Parikshit Ram; Nathalie Baracaldo; Sijia Liu 001 (NeurIPS, 2024)

Rethinking Machine Unlearning for Large Language Models. - Sijia Liu 001; Yuanshun Yao; Jinghan Jia; Stephen Casper; Nathalie Baracaldo; Peter Hase; Xiaojun Xu; Yuguang Yao; Hang Li 001; Kush R. Varshney; Mohit Bansal; Sanmi Koyejo; et al. (CoRR, 2024)

Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs. - Swanand Ravindra Kadhe; Farhan Ahmed; Dennis Wei; Nathalie Baracaldo; Inkit Padhi (CoRR, 2024)

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks. - Shuli Jiang; Swanand Ravindra Kadhe; Yi Zhou 015; Farhan Ahmed; Ling Cai; Nathalie Baracaldo (CoRR, 2024)

Mitigating Forgetting in LLM Supervised Fine-Tuning and Preference Learning. - Heshan Devaka Fernando; Han Shen; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Nathalie Baracaldo; Tianyi Chen (CoRR, 2024)

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble. - Zhiqi Wang; Chengyu Zhang; Yuetian Chen; Nathalie Baracaldo; Swanand Ravindra Kadhe; Lei Yu (CCS, 2025)

MAP: Multi-Human-Value Alignment Palette. - Xinran Wang; Qi Le; Ammar Ahmed; Enmao Diao; Yi Zhou 015; Nathalie Baracaldo; Jie Ding 002; Ali Anwar 001 (ICLR, 2025)

Towards a Re-evaluation of Data Forging Attacks in Practice. - Mohamed Suliman 002; Anisa Halimi; Swanand Ravindra Kadhe; Nathalie Baracaldo; Douglas J. Leith (USENIX Security Symposium, 2025)

Rethinking machine unlearning for large language models. - Sijia Liu 001; Yuanshun Yao; Jinghan Jia; Stephen Casper; Nathalie Baracaldo; Peter Hase; Yuguang Yao; Chris Yuhao Liu; Xiaojun Xu; Hang Li 001; Kush R. Varshney; Mohit Bansal; et al. (Nat. Mac. Intell., 2025)

Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning. - Changsheng Wang; Yihua Zhang; Jinghan Jia; Parikshit Ram; Dennis Wei; Yuguang Yao; Soumyadeep Pal; Nathalie Baracaldo; Sijia Liu 001 (CoRR, 2025)

EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation. - Jinghan Jia; Hadi Reisizadeh; Chongyu Fan; Nathalie Baracaldo; Mingyi Hong 001; Sijia Liu 001 (CoRR, 2025)

Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills. - Changsheng Wang; Chongyu Fan; Yihua Zhang; Jinghan Jia; Dennis Wei; Parikshit Ram; Nathalie Baracaldo; Sijia Liu 001 (CoRR, 2025)

Evaluating the Dynamics of Membership Privacy in Deep Learning. - Yuetian Chen; Zhiqi Wang; Nathalie Baracaldo; Swanand Ravindra Kadhe; Lei Yu (CoRR, 2025)