Website | Runhua Xu

Runhua Xu (许,润华)

the AI Security & Privacy Solutions group at
IBM Research - Almaden.

He holds Ph.D. degree in Information Security from
School of Computing and Information, University of Pittsburgh, worked with Prof. James Joshi.
Prior to that, he received M.S. in Computer Science from Beihang University, under the guidance of Prof. Bo Lang.
and B.E. in Software Engineering from Northwestern Polytechnical University.

curriculum vitae

runhua /dot/ xu /at/ pitt /dot/ com

runhua /dot/ xu /at/ outlook /dot/ com

Areas of Specialization

secure computing, privacy-preserving collaborative machine learning

applied cryptography, blockchain, access control

security and privacy issues in edge/cloud computing

Education

2015-2020, Ph.D. in Information Security, University of Pittsburgh, Pittsburgh, U.S.

2011-2014, M.S. in Computer Science, Beihang University, Beijing, China

2007-2011, B.E. in Software Engineering, Northwestern Polytechnical University, Xi’an, China

Publications

2022	Runhua Xu, Chao Li and James B.D. Joshi "DeSecAgg-FL: Privacy-Preserving Federated Learning with Decentralized Aggregators." (under peer review)	all-pub, manuscript-pub
2022	Runhua Xu, Chao Li and James Joshi. "Blockchain-based Transparency Framework for Privacy Preserving Third-party Services." IEEE Transactions on Dependable and Secure Computing (IEEE TDSC). IEEE 2022. [ pdf ] [ arXiv ] [ IEEE Early Access ]	all-pub, selected-pub, journal-pub
2022	Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig. "DeTrust-FL: Efficient Privacy-Preserving Federated Learning in Decentralized Trust Setting." In 2022 IEEE International Conference on Cloud Computing (IEEE CLOUD 2022), Hybrid event in Barcelona, Spain, IEEE 2022. (Best Paper Award) [ pdf ] [ arXiv ] [ slides ]	all-pub, selected-pub, conference-pub
2022	Nathalie Baracaldo and Runhua Xu. "Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?" Federated Learning: A Comprehensive Overview of Methods and Applications. Ludwig, H., Baracaldo, N. (eds). Springer, Cham. pp.281–312. 2022. [ Springer Link ]	all-pub, selected-pub, chapter-pub
2022	Runhua Xu, Nathalie Baracaldo, Yi Zhou, Annie Abay, and Ali Anwar. "Privacy-Preserving Vertical Federated Learning." Federated Learning: A Comprehensive Overview of Methods and Applications. Ludwig, H., Baracaldo, N. (eds). Springer, Cham. pp.417-438. 2022. [ Springer Link ]	all-pub, selected-pub, chapter-pub
2021	Runhua Xu, Nathalie Baracaldo and James B.D. Joshi. "Privacy-Preserving Machine Learning: Methods, Challenges and Directions ." (preprint) [ arXiv ]	all-pub, selected-pub, manuscript-pub
2021	Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig. "FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data." In Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security (AISec'21), pp. 181-192. 2021. [ arXiv ] [ S&P2021 Poster]	all-pub, selected-pub, workshop-pub
2021	Nathalie Baracaldo, Runhua Xu, Yi Zhou, Ali Anwar, and Heiko Ludwig. "Efficient private vertical federated learning." Patent Application 16/706,328, filed June 10, 2021. [ Link ]	all-pub, patent-pub
2021	Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig. "Privacy-preserving federated learning." U.S. Patent Application 16/682,927, filed May 13, 2021. [ Link ]	all-pub, patent-pub
2021	Runhua Xu, James B.D. Joshi and Chao Li. "NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-sourced Datasets." IEEE Transactions on Dependable and Secure Computing (IEEE TDSC). IEEE 2021. [ arXiv ] [ appendix ]	all-pub, selected-pub, journal-pub
2021	Chao Li, Balaji Palanisamy, Runhua Xu, Jinlai Xu and Jingzhe Wang. “SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform.” In 11th ACM Conference on Data and Application Security and Privacy (ACM CODASPY 21), Virtual Event, USA. [ pdf ] [ dataset ]	all-pub, selected-pub, conference-pub
2020	Runhua Xu and James B.D. Joshi “Revisiting Secure Computation Using Functional Encryption: Opportunities and Research Directions.” In The 2ed IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS), IEEE 2020. [ pdf ] [ slides ]	all-pub, selected-pub, conference-pub
2020	Chao Li, Balaji Palanisamy, Runhua Xu , Jian Wang, Jiqiang Liu “NF-Crowd: Nearly-free Blockchain-based Crowdsourcing.” 2020 International Symposium on Reliable Distributed Systems (SRDS20), Shanghai, China. IEEE 2020. [ pdf ]	all-pub, conference-pub
2020	Runhua Xu and James B.D. Joshi “Trustworthy and Transparent Third Party Authority.” ACM Transactions on Internet Technology (ACM TOIT). ACM 2020. [ pdf ]	all-pub, selected-pub, journal-pub
2019	Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar and Heiko Ludwig. "HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning." In 12th ACM Workshop on Artificial Intelligence and Security(AISec’19), November 15, 2019, London, United Kingdom. ACM 2019. [ pdf ]	all-pub, selected-pub, workshop-pub
2019	Runhua Xu, James B.D. Joshi and Prashant Krishnamurthy. “An Integrated Privacy Preserving Attribute Based Access Control Framework Supporting Secure Deduplication.” IEEE Transactions on Dependable and Secure Computing (IEEE TDSC). IEEE 2019. [ pdf ]	all-pub, selected-pub, journal-pub
2019	Runhua Xu, James B.D. Joshi and Chao Li. "CryptoNN : Training Neural Networks over Encrypted Data." In The 39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019), Dallas, USA. IEEE 2019. [ arXiv ] [ slides ]	all-pub, selected-pub, conference-pub
2019	Chao Li, Balaji Palanisamy, and Runhua Xu. “Scalable and Privacy-preserving Design of On/Off-chain Smart Contracts.” In The First International Workshop on Blockchain and Data Management (BlockDM 2019), Macau SAR, China, IEEE 2019. [ arXiv ] [ slides ]	all-pub, selected-pub, workshop-pub
2018	Runhua Xu, Balaji Palanisamy and James B.D. Joshi. “QueryGuard: Privacy-preserving Latency-aware Query Optimization for Edge Computing.” In 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications(TrustCom-18), New York, USA. 2018. [ pdf ] [ slides ]	all-pub, selected-pub, conference-pub
2017	Runhua Xu, James B.D. Joshi, Prashant Krishnamurthy and David Tipper. “Insider Threat Mitigation in Attribute based Encryption.” In 9th Annual National Cyber Summit (Research Track), Von Braun Center, Huntsville, AL, USA. 2017. [ pdf ] [ slides ]	all-pub, workshop-pub
2016	Runhua Xu and James B.D. Joshi. “Enabling Attribute Based Encryption as an Internet Service.” In 2016 IEEE 2nd International Conference on Collaboration and Internet Computing, Pittsburgh, USA. pp. 417-426.IEEE, 2016. [ pdf ] [ slides ]	all-pub, conference-pub
2016	Runhua Xu and James B.D. Joshi. “An Integrated Privacy Preserving Attribute Based Access Control Framework.” In 2016 IEEE 9th International Conference on Cloud Computing (IEEE CLOUD 2016) (Research Track), San Francisco, USA. pp. 68-76. IEEE, 2016 [ pdf ] [ slides ]	all-pub, conference-pub
2015	Runhua Xu, and Bo Lang. “A CP-ABE scheme with hidden policy and its application in cloud computing.” International Journal of Cloud Computing, Advanced Cloud and Big Data. vol. 4, no. 4, pp. 279-298. 2015. [ pdf ]	all-pub, journal-pub
2014	Bo Lang, Runhua Xu, and Yawei Duan. “Self-contained Data Protection Scheme Based on CP-ABE.” EBusiness and Telecommunications, Communications in Computer and Information Science. pp. 306-321. 2014. [ pdf ]	all-pub, journal-pub
2013	Runhua Xu, Yang Wang, and Bo Lang. “A Tree-Based CP-ABE Scheme with Hidden Policy Supporting Secure Data Sharing in Cloud Computing.” In Advanced Cloud and Big Data (CBD), 2013 International Conference on, Nanjing, China. pp. 51-57. IEEE, 2013. [ pdf ] [ slides ]	all-pub, conference-pub
2013	Bo Lang, Runhua Xu, and Yawei Duan. “Extending the ciphertext-policy attribute based encryption scheme for supporting flexible access control.” In Security and Cryptography (SECRYPT), 2013 International Conference on, Reykjavik, Iceland. pp. 1-11. IEEE, 2013. [ pdf ] [ slides ]	all-pub, conference-pub

Academia Services & Activities

Journal Review

IEEE Transactions on Information Forensics and Security(TIFS);
IEEE Transactions on Dependable and Secure Computing (TDSC);
IEEE Transactions on Industrial Informatics (TII);
IEEE Transactions on Services Computing (TSC);
IEEE Transactions on Emerging Topics in Computing (TETC);
IEEE Transactions on Green Communications and Networking (TGCN);
IEEE Security and Privacy;
IEEE Journal on Selected Areas in Communications (JSAC);
IEEE Access;
ACM Transactions on Internet Technology (TOIT);
Springer Neural Processing Letters (NEPL);
Journal of Computer Science and Technology;
Information Sciences (IS);

Conference Organization

IEEE International Conference on Collaboration and Internet Computing (CIC 2020, 2021, 2022)

IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS 2020, 2021, 2022)

IEEE International Conference on Cognitive Machine Intelligence (CogMI 2020, 2021, 2022)

PC Member/Conference Review/External Review

IEEE International Conference on Big Data (BigData 2016, 2019, 2021, 2022);
IEEE International Conference on Distributed Computing Systems (ICDCS 2017, 2019);
IEEE International Conference on Collaboration and Internet Computing (CIC 2016, 2017, 2018, 2019);
IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS 2019);
IEEE International Conference on Services Computing (SCC 2017);
IEEE International Conference on Smart Computing (SMARTCOMP 2019);
IEEE International Conference on Data Science in Cyberspace (DSC 2019);
ACM Conference on Data and Application Security and Privacy (CODASPY 2016, 2017);
ACM Symposium on Access Control Models and Technologies (SACMAT 2017, 2019, 2020);
ACM International Conference on Information and Knowledge Management (CIKM 2017);
International World Wide Web Conference (WWW 2019, 2020);