Chen Chen

PhD Student
Computer Science Department
Stony Brook University
Email:   

I am currently a PhD student at Stonybrook University, New York working at the Network Security and Applied Cryptography(NSAC) lab under the guidance of Prof. Radu Sion.

Publications

  • PD-DM: An efficient locality-preserving block device mapper with plausible deniability
    Chen Chen, Anrin Chakraborti, Radu Sion
    Proceedings on Privacy Enhancing Technologies 2019 (Under Submission)
    [pdf] [bibtex]

  • DataLair: An Efficient Block Device Mapper with Plausible Deniability
    Anrin Chakraborti, Chen Chen, Radu Sion
    Proceedings on Privacy Enhancing Technologies 2017 (PETS 2017)
    [pdf] [bibtex]

  • KXRay -- Introspecting the kernel for rootkit timing footprints
    Chen Chen, Darius Suciu, Radu Sion
    23rd ACM Conference on Computer and Communications Security (CCS 2016 Poster)
    [pdf] [bibtex]

  • DataLair - A Storage Block Device with Plausible Deniability
    Anrin Chakraborti, Chen Chen, Radu Sion
    23rd ACM Conference on Computer and Communications Security (CCS 2016 Poster)
    [pdf] [bibtex]

  • Quantitative Musings on the Feasibility of Smartphone Clouds
    Chen Chen, Moussa Ehsan, Radu Sion
    15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2015)
    [pdf]  [bibtex]

  • HIFS: History Independence for File Systems
    Sumeet Bajaj, Chen Chen, Abhishek Kumar, Radu Sion
    24th ACM Symposium on Operating Systems Principles (SOSP 2013 Poster)
    [pdf] [bibtex]

Education

Courses

CSE532     Theory of Database Systems     Fall 2012
CSE548     Analysis of Algorithms     Fall 2012
CSE527     Intro to Computer Vision     Fall 2012
CSE534     Fundamental of Computer Networks     Spring 2013
CSE502     Computer Architecture     Spring 2013
CSE547     Discrete Mathematics     Spring 2013

Projects

Plausible Deniability for Block Device
  • Datalair is a practical plausible deniability system built based on a new write-only ORAM. It reduces the complexity of the state of the art existing write-only ORAM by a factor of O(logN). When compared with existing approaches, DataLair is two orders of magnitude faster (and as efficient as the underlying raw storage) for public data accesses, and 2-5 times faster for hidden data accesses.

  • PD-DM is a new efficient device mapper with strong plausible deniability against multi-snapshot adversaries. It preserves locality and increases performance by ensuring most of its underlying writes are sequential. In a typical setup, throughputs are one order of magnitude (10-100x) faster than existing approaches.

Memory Mining
  • This project aims to detect the existence and location of specific instances of target data structure types in a VM by observing memory accesses and training for targetspecific timing-based signatures. We deploy the detection mechanisms to defeat kernel rootkits that "hide" their associated processes from existing snapshot-based detection methods. We introduce multiple signature variants and evaluate them for different kernel versions.

Smartphone Datacenter
  • Smartphone Datacenter looks insight into the power-performance tradeoff at scale for ARM and x86 architectures by quantifying the cost/performance ratio precisely enough to allow for a broader conclusion about the feasibility of deploying an ARM datacenter in next few years.

Green DIMM
  • Green DIMM is a system that aims at energy efficient memory management in OS.

HIFS for Flash
  • HIFS for flash is a file system designed for flash storage devices with a good balance between the history independent security and device life time.

Resume