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Secure Network Functions as a Service (2016 - Present)

Modern enterprise networks heavily rely on network functions for advanced traffic processing such as deep packet inspection, traffic classification, and load balancing. Recent advances in Network Function Virtualisation (NFV) have pushed forward the paradigm of migrating in-house network functions to third-party cloud providers as software-based services for reduced cost and increased scalability. Despite its benefits, such a new service model also raises security and privacy concerns, as traffic is now redirected and processed in an untrusted environment. My research in this area focuses on two directions: 1) enabling ubiquitous network functions over encrypted network traffic via practical cryptographic protocols [IEEE INFOCOM'16], [IEEE TDSC'21] or confidential computing [ACM CCS'19], [NDSS'21], 2) providing assurance for network function execution [IEEE ICNP'16], [ACM/IEEE ToN'18].

  • [IEEE TDSC'21] Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Joseph Liu, Ron Steinfeld, Amin Sakzad, and Dongxi Liu, “Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes”, IEEE Transactions on Dependable and Secure Computing, In Press, 2021.
  • [ISOC NDSS'21] Shangqi Lai, Xingliang Yuan, Joseph Liu, Xun Yi, Qi Li, Dongxi Liu, and Surya Nepal, “OblivSketch: Oblivious Network Measurement as a Cloud Service”, In the Network and Distributed System Security Symposium, 2021 (Acceptance ratio: 15%).
  • [ACM CCS'19] Huayi Duan, Cong Wang, Xingliang Yuan, Yajin Zhou, Qian Wang, and Kui Ren, “LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed”, in the 26th ACM Conference on Computer and Communications Security, 2019 (Acceptance ratio: 16%).
  • [ACM/IEEE ToN'18] Xingliang Yuan, Huayi Duan, and Cong Wang, “Assuring String Pattern Matching in Outsourced Middleboxes", IEEE/ACM Transactions on Networking (ToN), In Press, 2018.
  • [IEEE Network'18] Cong Wang, Xingliang Yuan, Cui Yong, and Kui Ren, “Towards Secure Outsourced Middlebox Services: Practices, Challenges, and Beyond", IEEE Network Magazine (IEEE Network), vol. 32, no. 1, page 166-171, 2018.
  • [IEEE ICNP'16] Xingliang Yuan, Huayi Duan, and Cong Wang, “Bringing Execution Assurances of Pattern Matching in Outsourced Middleboxes", In tin the 24th IEEE International Conference on Network Protocols, 2016 (Acceptance ratio: 20%).
  • [IEEE INFOCOM'16] Xingliang Yuan, Xinyu Wang, Jianxiong Lin, and Cong Wang, “Privacy-preserving Deep Packet Inspection in Outsourced Middleboxes”, in the 35th International Conference on Computer Communications, 2016 (Acceptance ratio: 19%).

    • Trustworthy Machine Learning (2019 - Present)

    Due to increasing popularity and rapid advancement of deep learning, public cloud service providers are promoting Machine Learning as a Service (MLaaS), e.g., AWS SageMaker. In the meantime, security and privacy issues of machine learning models, algorithms, and services are not fully understood and addressed in academia and industry. My research in this area focuses on three directions: 1) designing lightweight privacy-preserving machine learning systems [ESORICS'21], [IEEE TIFS'21], [IEEE TDSC'22-a], [IOS JCS], 2) investigating adversarial attacks and defenses on emerging ML paradigm like Graph Neural Networks (GNN) [ACM AsiaCCS'21], [IEEE ICDM'21], [ACM CIKM'21], transfer learning [IEEE TDSC'22-b], and 3) devising secure and efficient federated learning algorithms [IEEE INFOCOM'22], [IEEE TDSC'22-c].

  • [IOS JCS] Xiaoning Liu, Yifeng Zheng, Xingliang Yuan, and Xun Yi, “Deep Learning-Based Medical Diagnostic Services: A Secure, Lightweight, and Accurate Realization", Journal of Computer Security (JCS), Accepted, 2022.
  • [IEEE TDSC'22-c] Yifeng Zheng, Shangqi Lai, Yi Liu, Xingliang Yuan, Xun Yi, and Cong Wang, “Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization", IEEE Transactions on Dependable and Secure Computing, Accepted, 2022.
  • [IEEE TDSC'22-b] Bang Wu, Shuo Wang, Xingliang Yuan, Cong Wang, Carsten Rudolph, Xiangwen Yang, “Defeating Misclassification Attacks Against Transfer Learning", IEEE Transactions on Dependable and Secure Computing, Accepted, 2022.
  • [IEEE TDSC'22-a] Xiaoning Liu, Yifeng Zheng, Xingliang Yuan, and Xun Yi, “Securely Outsourcing Neural Network Inference to the Cloud with Lightweight Techniques", IEEE Transactions on Dependable and Secure Computing, Accepted, 2022.
  • [IEEE TIFS'21] Xiaoning Liu, Bang Wu, Xingliang Yuan, and Xun Yi, “Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge", IEEE Transactions on Information Forensics and Security, Accepted, 2021.
  • [IEEE INFOCOM'22] Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li, “The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining", in the 41st International Conference on Computer Communications, Accepted, 2022 (Acceptance ratio: 19.9%).
  • [ACM AsiaCCS'22] Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan, “Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realisation”, In the 17th ACM ASIA Conference on Computer and Communications Security , 2022 (First round acceptance ratio: 15%).
  • [IEEE ICDM'21] Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan, “Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications”, In the IEEE International Conference on Data Mining, 2021.
  • [ACM CIKM'21] He Zhang, Bang Wu, Xiangwen Yang, Chuan Zhou, Shuo Wang, Xingliang Yuan, and Shirui Pan, “Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks”, In the 30th ACM International Conference on Information and Knowledge Management, 2021.
  • [ESORICS'21] Xiaoning Liu, Yifeng Zheng, Xingliang Yuan, and Xun Yi, “MediSC: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service”, In the 26th European Symposium on Research in Computer Security, 2021 (Best Paper Award, Acceptance ratio: 21%).

    • Encrypted and Queryable Databases (2014 - Present)

    Encrypted databases are designed to fight against massive data breaches. They preserve database query functionalities over encrypted data directly without decryption. My research in this area focuses on four aspects: 1) enabling rich queries for encrypted databases [ESORICS'15], [IEEE TMM'16], [IEEE TIFS'17], [ACM AsiaCCS'19], 2) developing encrypted NoSQL data stores [ACM AsiaCCS'16], [ACM AsiaCCS'17], 3) designing efficient encrypted search schemes with less leakage[ACM CCS'18], [ACNS'20], [ACNS'21], [NDSS'21], and 4) exploring hardening techniques for encrypted databases [IEEE INFOCOM'19], [IEEE TKDE'21], [IEEE TIFS'21].

  • [IEEE TIFS'21] Lei Xu, Huayi Duan, Anxin Zhou, Xingliang Yuan, and Cong Wang, “Interpreting and Mitigating Leakage-abuse Attacks in Searchable Symmetric Encryption", IEEE Transactions on Information Forensics and Security, Accepted, 2021.
  • [IEEE TKDE'21] Viet Vo, Xingliang Yuan, Shi-Feng Sun, Joseph Liu, Surya Nepal, and Cong Wang, “ShieldDB: An Encrypted Document Database with Padding Countermeasures", IEEE Transactions on Knowledge and Data Engineering, Accepted, 2021.
  • [ISOC NDSS'21] Shi-Feng Sun, Ron Steinfeld, Shangqi Lai, Xingliang Yuan, Amin Sakzad, Joseph Liu, Surya Nepal, and Dawu Gu, “Practical Non-Interactive Searchable Encryption with Forward and Backward Privacy”, In the Network and Distributed System Security Symposium, 2021 (Acceptance ratio: 15%).
  • [ACNS'21] Viet Vo, Shangqi Lai, Xingliang Yuan, Joseph Liu, and Surya Nepal, "Towards Efficient and Strong Backward Private Searchable Encryption with Secure Enclaves", in the 19th International Conference on Applied Cryptography and Network Security, 2021 (First round acceptance ratio: 13/77= 16.8%).
  • [ACNS'20] Viet Vo, Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Surya Nepal, and Joseph K. Liu, "Accelerating Forward and Backward Private Searchable Encryption Using Trusted Execution", in the 18th International Conference on Applied Cryptography and Network Security, 2020 (Acceptance ratio: 21%).
  • [ACM AsiaCCS'19] Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Joseph K. Liu, Yuhong Liu, and Dongxi Liu, “GraphSE^2: An Encrypted Graph Database for Privacy-Preserving Social Search”, in the 14th ACM Asia Conference on Computer and Communications Security , 2019. (Acceptance ratio: 17%).
  • [IEEE INFOCOM'19] Lei Xu, Xingliang Yuan, Cong Wang, Qian Wang, and Chungen Xu, “Hardening Database Padding for Searchable Encryption”, in the 38th International Conference on Computer Communications, 2019. (Acceptance ratio: 19.7%).
  • [ACM CCS'18] Shi-Feng Sun, Xingliang Yuan, Joseph K. Liu, Ron Steinfeld, Amin Sakzad, Viet Vo, and Surya Nepal, “Practical Backward-Secure Searchable Encryption from Symmetric Puncturable Encryption”, in the 25th ACM Conference on Computer and Communications Security , 2018. (Acceptance ratio: 134/809 = 16.6%)
  • [IEEE TIFS'17] Xingliang Yuan, Xinyu Wang, Chenyun Yu, and Sarana Nutanong, “Privacy-preserving Similarity Joins Over Encrypted Data”, IEEE Transactions on Information Forensics and Security (TIFS), vol. 12, no. 11, page 2763-2775, 2017.
  • [ACM AsiaCCS'17] Xingliang Yuan, Yu Guo, Xinyu Wang, Cong Wang, Baochun Li, and Xiaohua Jia, “EncKV: An Encrypted Key-value Store with Rich Queries”, in the 12th ACM Asia Conference on Computer and Communications Security , 2017. (Acceptance ratio: 18.6%) Extended Version in IEEE TPDS.
  • [IEEE TMM'16] Xingliang Yuan, Xinyu Wang, Cong Wang, and Kui Ren, “Enabling Secure and Fast Indexing for Privacy-assured Healthcare Monitoring via Compressive Sensing”, IEEE Transactions on Multimedia (TMM), vol. 18, no. 10, page 2002-2014, 2016.
  • [ACM AsiaCCS'16] Xingliang Yuan, Xinyu Wang, Cong Wang, Chen Qian, and Jianxiong Lin, “Building an Encrypted, Distributed, and Searchable Key-value Store”, in the 11th ACM Asia Conference on Computer and Communications Security , 2017. (Acceptance ratio: 20.9%)
  • [ESORICS'15] Xingliang Yuan, Helei Cui, Xinyu Wang, and Cong Wang, “Enabling Privacy-assured Similarity Retrieval over Millions of Encrypted Records”, in the 20th European Symposium on Research in Computer Security , 2015. (Acceptance ratio: 19.8%)