myself

Boxiang Dong, Ph.D

Associate Professor

Department of Computer Science

Montclair State University

Office: CCIS 327R

Phone: 973-655-4093

Email: dongb AT montclair DOT edu
A Brief Bio

I am an associate professor in the Computer Science Department at Montclair State University.

Prior to joining MSU, I received my Ph.D in Computer Science in 2016 from Department of Computer Science of Stevens Institute of Technology, which is located in NJ, USA. My Ph.D advisor is Prof. Wendy Wang. The title of my Ph.D dissertation is "Privacy-preserving and Authenticated Data Cleaning on Outsourced Databases" [PDF] [Slides].

In 2011, I received my Bachelor degree from Dalian University of Technology, China. In that time period, I worked with Prof. Weifeng Sun on network security.

This is my CV (last updated: 10/2021).

Research interests:

  • Cybersecurity
    • authentication of outsourced computing
    • privacy-preserving data mining
    • intrusion detection
  • Big data analytics
    • risk control
    • anomaly detection
    • deep learning
News
2023-03Our paper has been accepted by Creativity Research Journal!
2023-01Our REU summer program is accepting new applications for Summer 2023. Website!
2022-09I have been promoted to tenured and associate professor at MSU!
Publications

Journal

  1. Kai Wang, Boxiang Dong.
    Testing Computational Assessment of Idea Novelty in Crowdsourcing.
    Creativity Research Journal, 2023.
    [Paper]
  2. Boxiang Dong, Zhengzhang Chen, Hui (Wendy) Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, Haifeng Chen.
    Anomalous Event Sequence Detection.
    IEEE Intelligent Systems, 2020.
    [Preface]
  3. Bharath K Samanthula, Divya Karthikeyan, Boxiang Dong, K Anitha Kumari.
    ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services.
    MDPI Cryptography, 2020
    [Paper]
  4. Allen Yang, Boxiang Dong, Dawei Li, Weiefeng Sun, Bharath K. Samanthula.
    DeepICU: Imbalanced classification by using Deep Neural Networks for Network Intrusion Detection.
    International Journal of Big Data Intelligence, 2020.
    [Preface]
  5. En Wang, Dawei Li, Boxiang Dong, Huan Zhou, Michelle Zhu.
    Flat and Hierarchical System Deployment for Edge Computing Systems.
    Future Generation Computer Systems, 2020. (Impact Factor = 5.768)
    [Paper]
  6. Bo Zhang, Boxiang Dong, Hui (Wendy) Wang.
    Integrity Authentication for SQL Query Evaluation on Outsourced Databases: A Survey.
    IEEE Transactions on Knowledge and Data Engineering, 2019. (Impact Factor = 3.857)
    [Paper]
  7. Bo Zhang, Boxiang Dong, Hui (Wendy) Wang.
    CorrectMR: Authentication of Distributed SQL Execution on MapReduce.
    IEEE Transactions on Knowledge and Data Engineering, 2019. (Impact Factor = 3.857)
    [Paper] [Bibtex]
  8. Boxiang Dong, Hui (Wendy) Wang.
    Efficient Authentication of Approximate Record Matching for Outsourced Databases.
    Information System Frontiers, 2018. (Impact Factor = 3.232)
    [Paper] [Bibtex]
  9. Boxiang Dong, Hui (Wendy) Wang.
    Secure Partial Encryption with Adversarial Functional Dependency Constraints in the Database-as-a-Service Model.
    Data & Knowledge Engineering, 2018. (Impact Factor = 1.694)
    [Paper] [Bibtex]
  10. Boxiang Dong, Hui (Wendy) Wang, Anna Monreale, Dino Pedreschi, Fosca Giannotti, Wenge Guo.
    Authenticated Outlier Mining for Outsourced Databases..
    The IEEE Transactions on Dependable and Secure Computing (TDSC). 2017. (Impact Factor = 2.926)
    [Paper] [Bibtex]
  11. Boxiang Dong, Ruilin Liu, Hui (Wendy) Wang.
    Trust-but-Verify: Verifying Result Correctness of Outsourced Frequent Itemset Mining.
    IEEE Transactions on Services Computing. 2015. (Impact Factor = 2.520)
    [Paper][Bibtex]
  12. Weifeng Sun, Juanyun Wang, Boxiang Dong, Mingchu Li, Zhenquan Qin.
    A Mediated RSA-based End Entity Certificates Revocation Mechanism with Secure Concern in Grid.
    International Journal of Information Processing and Management (IJIPM). 2010. (Impact Factor = 2.391)
    [Paper] [Bibtex]

Conference

  1. Eric Ji, Boxiang Dong, Bharath K Samanthula, Na Zhou. 2D-FACT: Dual-Domain Fake Image Detection Against Text-to-Image Generative Models.
    IEEE MIT URTC (Undergraduate Research Technology Conference), Boston, MA, USA, 2023.
  2. Gowri Pandian Sundarapandi, Salma Bokhary, Bharath K Samanthula, Boxiang Dong.
    A Probabilistic Approach for Secure and Verifiable Computation of kNN Queries in Cloud.
    IEEE Cloud Summit, Washington DC, USA, 2023.
  3. Kai Wang, Boxiang Dong.
    Finding and Testing Diverse Stimuli for Enhancing Creative Ideation.
    Academy of Management Proceedings 2022. Seattle, WA.
  4. Louay Ahmad, Boxiang Dong, Bharath Samanthula, Ryan (Yang) Wang, Bill (Hui) Li.
    Towards Trustworthy Outsourced Deep Neural Networks.
    IEEE Cloud Summit, Long Island, NY, 2021.
  5. Boxiang Dong, Bo Zhang, Hui (Wendy) Wang.
    VeriDL: Integrity Verification of Outsourced Deep Learning Services.
    The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Virtual, 2021.
  6. Weifeng Sun, Shumiao Yu, Boxiang Dong.
    VDGAN: A Collaborative Filtering Framework Based on Variational Denoising with GANs.
    International Joint Conference on Neural Networks, 2021.
  7. Boxiang Dong, Hui (Bill) Li, Yang (Ryan) Wang, and Rami Safadi.
    2D-ATT: Causal Inference for Mobile Game Organic Installs with 2-Dimensional Attentional Neural Network
    IEEE Conference on Big Data. Virtually Online. 2020.
    [Paper][Bibtex]
  8. Weifeng Sun, Shumiao Yu, Jin Yang, Boxiang Dong.
    A Novel Collaborative Filtering Framework Based on Variational Self-Attention GAN
    IEEE Global Communications Conference (GLOBECOM 2020). Taipei, Taiwan.
  9. Bo Zhang, Boxiang Dong, Hui (Wendy) Wang.
    AuthPDB: Authentication of Probabilistic Queries onOutsourced Uncertain Data
    ACM Conference on Data and Application Security and Privacy (CODASPY 2020). New Orleans, LA.
    [Paper]
  10. Dawei Li, Chigozie Asikaburu, Boxiang Dong, Huan Zhou, Sadoon Azizi.
    Towards Optimal System Deployment for Edge Computing: A Preliminary Study.
    IEEE International Conference on Computer Communications and Networks (ICCCN), 2020.
    [Paper]
  11. Boxiang Dong, Aparna S. Varde, Danilo Stevanovic, Jiayin Wang and Liang Zhao.
    Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition.
    IEEE International Conference on Big Data Intelligence and Computing (IEEE DataCom 2019). Kaohsiung, Taiwan.
    [Paper][Bibtex]
  12. Boxiang Dong, Wendy Hui Wang, Aparna S. Varde, Dawei Li, Bharath K. Samanthula, Weifeng Sun and Liang Zhao.
    Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss.
    IEEE International Conference on Big Data Intelligence and Computing (IEEE DataCom 2019). Kaohsiung, Taiwan.
    [Paper][Bibtex]
  13. Weifeng Sun, Minghan Jia, Shumiao Yu, Boxiang Dong, Xinyi Li.
    An SVM Based Secural Image Steganography Algorithm for IoT.
    International Symposium on Cyberspace Safety and Security, Guangzhou, China, 2019.
    [Paper]
  14. Weifeng Sun, Zun Wang, Guanghao Zhang, Boxiang Dong.
    MACCA: A SDN based Collaborative Classification Algorithm for QoS guaranteed Transmission on IoT.
    The 15th International Conference on Advanced Data Mining and Applications, Dalian, China, 2019.
    [Paper]
  15. Bo Zhang, Boxiang Dong, Hui (Wendy) Wang., Hui Xiong
    Trust-but-Verify: Result Verification of Federated Deep Learning.
    Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, Anchorage, Alaska. 2019. (Co-located with KDD'19)
    Best Paper Award
  16. Bharath K. Samanthula, Salha Albehairi, Boxiang Dong.
    A Privacy-Preserving Framework for Collaborative Association Rule Mining in Cloud.
    IEEE International Conference on Cloud and Fog Computing Technologies and Applications. Washington DC. 2019.
  17. Yanying Li, Hui (Wendy) Wang, Boxiang Dong.
    PIVOT: Privacy-preserving Outsourcing of Text Data for Word Embedding.
    AAAI Spring Symposium on Privacy-Enhancing Artificial Intelligence and Language Technologies. 2019.
  18. Dawei Li, Boxiang Dong, En Wang, Michelle Zhu.
    A Study on Flat and Hierarchical System Deployment for Edge Computing.
    IEEE Computing and Communication Workshop and Conference (CCWC). Las Vegas, NV. 2019.
  19. Yanying Li, Haipei Sun, Boxiang Dong, Wendy Hui Wang.
    Cost-efficient Data Acquisition on Online Data Marketplaces for Correlation Analysis.
    International Conference on Very Large Data Bases (VLDB). Los Angeles, CA. 2019.
    [Paper][Bibtex]
  20. Manish Puri, Aparna S. Varde, Boxiang Dong.
    Pragmatics and Semantics to Connect Specific Local Laws with Public Reactions.
    IEEE 2018 International Conference on Big Data. Seattle, WA. 2018. (Poster Paper)
    [Paper][Bibtex]
  21. Haipei Sun, Boxiang Dong, Wendy Hui Wang, Ting Yu, Zhan Qin.
    Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy.
    IEEE 2018 International Conference on Big Data. Seattle, WA. 2018. (Acceptance Rate: 99/518 = 19.1%)
    [Paper][Bibtex]
  22. Kai Wang, Boxiang Dong, Junjie Ma.
    Towards Computational Assessment of Idea Novelty.
    Hawaii International Conference on System Sciences (HICSS). Grand Wailea, Maui. 2019.
    [Paper][Bibtex]
  23. Haipei Sun, Boxiang Dong, Bo Zhang, Hui (Wendy) Wang, Murat Kantarcioglu.
    Sensitive Task Assignments in Crowdsourcing Markets with Colluding Workers.
    IEEE International Conference on Data Engineering (ICDE 2018). Paris, France. 2018.
    [Paper] [Bibtex]
  24. Bo Zhang, Boxiang Dong, Hui (Wendy) Wang.
    AssureMR: Verifiable SQL Execution on MapReduce.
    IEEE International Conference on Data Engineering (ICDE 2018). Paris, France. 2018.
    [Bibtex]
  25. Boxiang Dong, Zhengzhang Chen, Hui Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li and Haifeng Chen.
    Efficient Discovery of Abnormal Event Sequences in Enterprise Security Systems..
    The ACM International Conference on Information and Knowledge Management (CIKM). Pan Pacific, Singapore. 2017. (Acceptance Rate: 171/820=21%).
    [Paper] [Bibtex]
  26. Boxiang Dong, Hui (Wendy) Wang.
    EARRING: Efficient Authentication of Outsourced Record Matching..
    The IEEE International Conference on Information Reuse and Integration (IRI). San Diego, CA. 2017. (Acceptance Rate: 29%).
    Best Paper Award
    [Paper][Bibtex]
  27. Bo Zhang, Boxiang Dong, Hui (Wendy) Wang.
    Budget-constrained Result Integrity Verification of Outsourced Data Mining Computations.
    31th Annual IFIP WG 11.3 Conference on Data and Application Security and Privacy (DBSec). Philadelphia, PA. 2017.
    [Paper][Bibtex]
  28. Changjiang Cai, Haipei Sun, Boxiang Dong, Bo Zhang, Ting Wang, Hui (Wendy) Wang.
    Pairwise Ranking Aggregation by Non-interactive Crowdsourcing with Budget Constraints.
    The IEEE International Conference on Distributed Computing Systems (ICDCS). Atlanta, GA. 2017.
    [Paper] [Bibtex]
  29. Yanying Li, Boxiang Dong, Dominik Jedruszczak, Hui (Wendy) Wang.
    Budget-constrained High-quality Data Purchase on Data Markets.
    The SIAM SDM'17 WinDS Workshop. Houston, TX. 2017.
    [Bibtex]
  30. Boxiang Dong, Hui (Wendy) Wang.
    Frequency-hiding Dependency-preserving Encryption for Outsourced Databases.
    The IEEE International Conference on Data Engineering (ICDE). San Diego, CA. 2017. (Acceptance Rate: 292/1395 = 20.9%).
    [Paper][Bibtex]
  31. Boxiang Dong, Hui (Wendy) Wang.
    ARM: Authenticated Approximate Record Matching for Outsourced Databases.
    The IEEE International Conference on Information Reuse and Integration (IRI). Pittsburgh, PA. 2016. (Acceptance Rate: 25.6%).
    [Paper][Bibtex]
  32. Boxiang Dong, Hui (Wendy) Wang, Jie Yang.
    Secure Data Outsourcing with Adversarial Data Dependency Constraints.
    The IEEE International Conference on Big Data Security on Cloud (BigDataSecurity). New York. 2016.
    [Paper][Bibtex]
  33. Boxiang Dong, Ruilin Liu, Hui (Wendy) Wang.
    Prada: Privacy-preserving Data-Deduplication-as-a-Service.
    ACM International Conference on Information and Knowledge Management (CIKM). Shanghai, China. 2014. (Acceptance Rate: 25/123=20%).
    [Paper][Bibtex]
  34. Boxiang Dong, Ruilin Liu, Hui (Wendy) Wang.
    Integrity Verification of Outsourced Frequent Itemset Mining with Deterministic Guarantee.
    IEEE International Conference on Data Mining (ICDM). Dallas, Texas. 2013. (Acceptance Rate: 159/809 = 19.7%).
    [Paper] [Bibtex]
  35. Boxiang Dong, Ruilin Liu, Hui (Wendy) Wang.
    Result Integrity Verification of Outsourced Frequent Itemset Mining.
    27th Annual IFIP WG 11.3 Conference on Data and Application Security and Privacy (DBSec). Newark, NJ. 2013.
    [Paper][Bibtex]
  36. Weifeng Sun, Boxiang Dong, Zhenquan Qin, Juanyun Wang, Mingchu Li.
    A Low-Level Security Solving Method in Grid.
    Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). Darmstadt, Germany. 2010.
    [Paper] [Bibtex]

Patent

  • Zhengzhang Chen, Lu-An Tang, Boxiang Dong, Guofei Jiang, Haifeng Chen.
    Graph-based Instrusion Detection Using Process Traces. (Publication number: WO2017019391 A1).
Research Projects

Causal Inference with 2-Dimensional Attentional Neural Network

We propose a novel attention mechanism to reveal the information flow in the generative process of the target variable and quantify the contribution of each factor.

The evaluation on mobile gaming datasets discover sparkling knowledge about the driving factors of organic installs.

  • Related publications:
    • 2D-ATT: Causal Inference for Mobile Game Organic Installs with 2-Dimensional Attentional Neural Network. IEEE International Conference on Big Data. 2020. Virtual Conference. [Paper][Bibtex][Presentation]

    • Intrusion Detection by Using Deep Neural Networks

    We propose a deep neural network infrastructure to detect network intrusion attacks.

    To cope with the imbalanced distribution of various types of attacks, we propose a novel loss function that eliminates the bias towards the majority class.

  • Related publications:
    • Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss. IEEE International Conference on Big Data Intelligence and Computing (IEEE DataCom 2019). Kaohsiung, Taiwan. [Paper][Bibtex][Presentation]

    • Privacy-preserving Crowdsourcing

    We protect the privacy in the setting of crowdsourcing in two ways.

    First, we protect task privacy by designing a task assignment strategy that minimizes the risk of information disclosure in the existence colluding workers.

    Second, we protect worker privacy by designing an answer perturbation mechanism to provide local differential privacy.

  • Related publications:
    • Sensitive Task Assignments in Crowdsourcing Markets with Colluding Workers. IEEE International Conference on Data Engineering (ICDE 2018). Paris, France. 2018. [Paper] [Bibtex]
    • Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy. IEEE 2018 International Conference on Big Data. Seattle, WA. 2018. (Acceptance Rate: 99/518 = 19.1%) [Bibtex]

    • Integrity Verification of Outsourced Approximate Record Matching

    We design a new authentication and indexing data structure--Merkle Bed Tree (MB-Tree in short), which obtains Merkle tree's authentication functionality and preserves Bed tree's indexing property.

    The server constructs verification objects (VOs) by traversing the MB-Tree and visiting the Euclidean space to prove the result's soundness and completeness.

    The client's verification cost is very small because of MB-tree's pruning power and the cheap Euclidean distance computation cost.

  • Related publications:
    • Efficient Authentication of Approximate Record Matching for Outsourced Databases.
      Information System Frontiers, 2018. (Impact Factor = 3.232)
      [Paper] [Bibtex]
    • EARRING: Efficient Authentication of Outsourced Record Matching.. The IEEE International Conference on Information Reuse and Integration (IRI). San Diego, CA. 2017. (Acceptance Rate: 29%). Best Paper Award [Paper][Bibtex]
    • ARM: Authenticated Approximate Record Matching for Outsourced Databases. The IEEE International Conference on Information Reuse and Integration (IRI). Pittsburgh, PA. 2016. (Acceptance Rate: 25.6%). [Paper][Bibtex]

    • Frequency-hiding Dependency-preserving Encryption for Outsourced Databases

    We define two security models: &alpha-security against frequency-analysis attack, and indistinguishability against functional dependency preserving chosen plaintext attack (IND-FCPA).

    We design a frequency-hiding FD-preserving probabilistic encryption scheme that provides strong security guarantee, while preserves the utility in the encrypted dataset.

  • Related publications:
    • Frequency-hiding Dependency-preserving Encryption for Outsourced Databases. IEEE International Conference on Data Engineering (ICDE). San Diego, CA. 2017. [Paper] [Bibtex]

    • Efficient Intrusion Detection in Large Enterprise Networks

    We model the system events in every host as a multipartitie graph of interactions between processes, sockets, and files.

    We apply random walk on the graph to learn the routine behavior of each system entity based on a transition probability model.

    We label an event sequence as abnormal if the behavior of any involved entity largely deviates from its routine role.

  • Related publications:
    • Efficient Discovery of Abnormal Event Sequences in Enterprise Security Systems. The ACM International Conference on Information and Knowledge Management (CIKM). Pan Pacific, Singapore. 2017. [Paper] [Bibtex]
  • Patent application:
    • Graph-based Instrusion Detection Using Process Traces. (Publication number: WO2017019391 A1).

    • Privacy-preserving Data Deduplication-as-a-Service.

    In order to defend against the frequency-analysis attack in the outsourced data-deduplication services, we propose two approaches.

    • The LSHB approach based on Locality-Sensitive Hashing (LSH): The constructed LSH values preserve the string similarity, while they are of the same frequency groupwise.
    • The EHS approach encode strings as Euclidean points. The points of similar strings are close in the Euclidean space. At the same time, they have uniform frequency distribution.
      • LSHB approach EHS approach
    • Related publications:
      • Prada: Privacy-preserving Data-Deduplication-as-a-Service. ACM International Conference on Information and Knowledge Management. Shanghai, China. 2014. [Paper] [Bibtex]

    Integrity Verification of Outsourced Frequent Itemset Mining.

    We aim at verifying if the untrusted server returns the correct and complete frequent itemset mining result.

    To accomplish the goal, we require the server to return cryptographic proofs to show the support of the itemsets. The proof is based on the Secure Set Intersection Protocol.

    Related publications:

    • Trust-but-Verify: Verifying Result Correctness of Outsourced Frequent Itemset Mining. IEEE Transactions on Services Computing. 2015.[Paper][Bibtex]
    • Integrity Verification of Outsourced Frequent Itemset Mining with Deterministic Guarantee. IEEE International Conference on Data Mining(ICDM). Dallas, Texas. 2013. [Paper] [Bibtex]
    • Result Integrity Verification of Outsourced Frequent Itemset Mining. 27th Annual IFIP WG 11.3 Conference, DBSec. Newark, NJ. 2013. [Paper] [Bibtex]
    Professional Services
    Conference and Journal Reviewer
    • Scientific Programming for Multimodal Big Data, Guest Editor
    • Distributed and Parallel Databases
    • IEEE Transactions on Emerging Topics in Computational Intelligence
    • IEEE Transactions on Dependable and Secure Computing (TDSC), 2019
    • IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019
    • IEEE Annual Conference of Industrial Electronics Society, 2019
    • Engineering Applications of Artificial Intelligence, 2019
    • Journal of Computer Science and Technology, 2019
    • IEEE International Conference on Big Data, 2018
    • Journal of King Saud University - Computer and Information Sciences, 2018
    • ACM Transactions on Privacy and Security, 2018
    • International Conference on Data Mining (ICDM), 2018
    • Frontiers in Big Data
    • Journal of ICT Research and Applications, 2018
    • Journal of Information Security and Applications, 2018
    • Computers & Security, 2018
    • IEEE Transactions on Service Computing (TSC), 2018
    • SIAM International Conference on Data Mining (SDM), 2018
    • Journal of Computer and Communications, 2018
    • Advances in Science, Technology and Engineering Systems Journal, 2018
    • MDPI, 2018
    • Journal of Computer and Communications, 2018
    • IEEE Transactions on Information Forensics & Security (T-IFS), 2017
    • IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017
    • IEEE Access, 2017
    • IEEE International Conference on Big Data, 2017
    • Entropy Special Issue on Information Theory in Machine Learning and Data Science, 2017
    • Information Systems Journal, 2017
    • Future Internet, 2017
    • International Conference on Information System Security (ICISS), 2017
    • Journal of Computer and Communications, 2017
    • Mobile Information Systems, 2016
    • International Journal of Distributed Sensor Network, 2016
    • AAAI Conference on Artificial Intelligence, 2016
    • International Joint Conference on Artificial Intelligence, 2016
    • International Workshop on the Web and Databases, 2016
    • International Conference on Communications in China, 2015
    • International Conference on Data Science and Advanced Analytics, 2015
    • International Conference on Extending Database Technology, 2014
    Conference PC Member
    • AAAI Conference on Artificial Intelligence, 2022.
    • IEEE International Conference on Machine Learning and Applications, 2021.
    • International Conference on Web Search and Data Mining (WSDM), 2021.
    • ACM International Conference on Information and Knowledge Management (CIKM), 2021.
    • IEEE International Conference on Big Data, 2021.
    • European Machine Learning and Data Mining Conference (ECML-PKDD), 2021.
    • International Conference on Tools with Artificial Intelligence(ICTAI), 2019.
    • Security and Privacy in Edge Computing Workshop (EdgeSP), 2019.
    • International Conference on Information Systems Security, 2019.
    • IEEE International Conference on Information Reuse and Integration for Data Science (IRI), 2018.
    • Security and Privacy in Edge Computing Workshop (EdgeSP), 2018.
    • International Workshop on Personal Analytics and Privacy (PAP), in conjunction with ECML PKDD, 2017, 2018.
    Teaching Experiences

    • CSIT212 Data Structures and Algorithms Fall 2017, 2018, Spring 2018

    • CSIT111 Fundamentals of Programming I Fall 2017

    • CSIT100 Introduction to Computer Science Spring 2017

    • CSIT345 Operating Systems Spring 2017

    • Introduction to Cybersecurity (part of Stevens Pre-College Programs) Summer 2016

    Working Experiences

    • Associate Professor
    Montclair State University, Montclair, NJ

    Sep.2022 - Now

    • Assistant Professor
    Montclair State University, Montclair, NJ

    Jan.2017 - Aug.2022

    • Research Intern at NEC Labs America, Princeton NJ
    Project: Anomaly Detection in Large Enterprise Network

    Sep.2014 - Dec.2014
    Mentor: Dr. Zach Chen

    • Research Intern at NEC Labs America, Princeton NJ
    Project: Rule-based Mining in Multivariate Time Series

    May.2015 - Aug.2015
    Mentor: Dr. Tan Yan
    Presentations
    • "2D-ATT: Causal Inference for Mobile Game Organic Installs with 2-Dimensional Attentional Neural Network". IEEE BigData (Virtually), 2020. [Slides]
    • "AuthPDB: Authentication of Probabilistic Queries onOutsourced Uncertain Data." CODASPY (Virtually), 2020. [Slides]
    • "Cost-efficient Data Acquisition on Online Data Marketplaces for Correlation Analysis." VLDB, Los Angeles, CA. 2019.[Slides]
    • "Towards Computational Assessment of Idea Novelty". HICSS. Maui, Hawaii. 2019. [Slides]
    • "Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy". IEEE Big Data. Seattle, Washington. 2018. [Slides]
    • "EARRING: Efficient Authentication of Outsourced Record Matching". IRI. San Diego, California. 2017. [Slides]
    • "Pairwise Ranking Aggregation by Non-interactive Crowdsourcing with Budget Constraints". ICDCS. Atlanta, Georgia. 2017.
    • "Budget-constrained High-quality Data Purchase on Data Markets". WinDS (collated with SDM). Houston, Texas. 2017.
    • "Frequency-hiding Dependency-preserving Encryption for Outsourced Databases". ICDE'17. San Diego, California. 2017.[Slides]
    • "ARM: Authenticated Approximate Record Matching for Outsourced Databases". IEEE IRI, 2016. Pittsburgh, Pennsylvania. 2016.[Slides]
    • "Secure Data Outsourcing with Adversarial Data Dependency Constraints". IEEE BigDataSecurity 2016. Columbia University, New York. 2016.[Slides]
    • "Privacy-preserving Data Deduplication-as-a-Service". Stevens Graduate Research Conference. Hoboken, NJ. 2016.
    • "Integrity Verification of Outsourced Frequent Itemset Mining with Deterministic Guarantee". ICDM. Dallas, TX. 2013.[Slides]
    • "Result Integrity Verification of Outsourced Frequent Itemset Mining". DBSec 2013. Rutgers, NJ. 2013.
    • "A Novel Grid Resource Scheduling Model Based on Extended Second Price Sealed Auction". International Symposium on Parallel Architectures, Algorithms and Programming. Dalian, Liaoning, China. 2010.
    Funding and Awards
    • NSF REU SITE: Enhancing Undergraduate Research Experiences in Cybersecurity and Privacy-Enhanced Technologies, $395,470, 2021-2024. (My role: Co-PI) [Abstract]
    • Best Paper Award at Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, 2019 (Co-located with KDD'19).
    • Best Paper Award at IEEE 18th International Conference on Information Reuse and Integration, 2017.
    • Stevens Outstanding Graduate Student Award, 2017.
    • NEC Excellent Invention Award for the patent application "Graph-based Intrusion detection Using Process Traces ", 2017.