I now work at Apple. In 2016, I obtained my Ph.D. in Computer Science at Boston University. My focus was in applied cryptography, data/cloud privacy. In general, my work focuses on the development of privacy-enhancing technologies that minimize the information being revealed when outsourcing massive datasets in cloud-based environments.


I investigate data security/privacy, privacy-enhancing learning technologies, and data analytics.

Selected Publications

  • NED: An Inter-Graph Node Metric based on Edit Distance
    with Haohan Zhu, George Kollios, VLDB 2017, Proceeding version  Poster.
  • Top-k Query Processing on Encrypted Databases with Strong Security Guarantees
    with Haohan Zhu, George Kollios arXiv version: CoRR abs/1510.05175.  Poster.
  • GRECS: Approximate Shortest Distance Queries on Encrypted Graphs
    with Seny Kamara, Kobbi Nissim, and George Kollios. 22nd ACM CCS, Denver, Colorado, USA, October 2015
    Proceeding Version, Full version, PDF Slides, Poster
  • Privacy Preserving Similarity Evaluation of Time Series Data,
    with Haohan Zhu, and George Kollios. 17th EDBT/ICDT, Athens, Greece, March 2014.
    Proceeding Version, Poster, Code
  • Computational Fuzzy Extractors
    with Ben Fuller, and Leo Reyzin. 19th IACR ASIACRYPT, Bangalore, India, December 2013
    Proceeding Version, Full version


Privacy-preserving queries on encrypted databases.

The objective is to design provably secure and scalable schemes for encrypting large-scale databases without losing the ability to query them. Supported by The Modular Approach to Cloud Security (MACS).
Graph databases: I investigate graph encryption. Ideally, a graph encryption scheme should encrypt a graph with support for various graph queries. In this paper, we proposed GRECS that can support approximate shortest distance queries on encrypted graph. See the blog entry. (Collaboration with Seny Kamara)
Relational databases: I also investigate encryption schemes that can provide more functionalities on encrypted relational databases. The challenge is to balance the three objectives of privacy / security, functionality / utility / accuracy, and performance / scalability. For example, in this we propose an efficient secure top-k queries processing on encrypted database.

Secure Multiparty Computation and Data Mining/Learning.

Multiparty computation: Motivated by the data privacy concerns in today's internet, I am exploring scalable privacy-preserving data mining/machine learning protocols where each party only learns the output of the algorithm and nothing else. In particular, I consider mostly data mining/learning algorithms that can scale to massive amount of datasets. For example, I proposed a privacy-preserving protocol for computing the distance between two time-series (see here). I am also studying privacy-preserving clustering algorithms. (Collaboration with RSA labs)

Data Mining: Graph De-anonymization.

Node similarity: I am interested in node similarity measures, a fundamental problem in graph data analytics that can be used for graph de-anonymization. Existing node similarity measurements focus on evaluating how similar between two nodes in the same graph. In this project, we investigate methods that measure similarity between nodes in different graphs (inter-graph nodes). In particular, we are interested in defining new distance metrics that are based on the neighborhood topology of two nodes. (see here)


I started to work at Apple Inc. since 2016, where I conduct my research and apply them to practice.
Previously, I received my Ph.D. at Boston University, where I was a member of DBLab and BUSec in the Department of Computer Science at BU. My Ph.D. dissertation is "Privacy-Preserving Queries On Encrypted Databases".
I spent a some time at Microsoft Research in 2014. See my guest posting on discussing about graph privacy: "Graph Encryption: Going Beyond Encrypted Keyword Search", on Seny Kamara's Blog

Journal/Conference Reviewer

IEEE TKDE 2014/2015, IEEE ICDE 2013/2014, SIGMOD 2013/2014
VLDB 2013/2014/2015, EDBT/ICDT 2015/2016