Welcome to Chen’s Homepage!

Chen Chen

Research Scientist 

at Meta 

Bellevue, Washington, United States 

About Me

I am currently a Research Scientist at Meta working on AI Privacy. I received my PhD degree from the department of Computer Science, Stony Brook University, New York, where I worked on the Network Security and Applied Cryptography (NSAC) lab under the guidance of Prof. Radu Sion.

My area of interests are system security, data privacy, embedded storage system, machine learning and clouds. 

Before coming to US, I received my Bachelor degree at Tsinghua University, China.

Here is my CV

Education

Doctor of Philosophy (Ph.D.), Computer Science                                                   Stony Brook University

Advisor: Radu Sion

Bachelor's Degree, Automation                                                                                      Tsinghua University

Advisor: Jie Zhou

Publication


SOK: Plausibly deniable storage

Chen Chen, Xiao Liang, Bogdan Carbunar, Radu Sion [pdf] [bibtex]    

Proceedings on Privacy Enhancing Technologies 2022 (PETS 2022)


PEARL: Plausibly Deniable Flash Translation Layer using WOM coding

Chen Chen, Anrin Chakraborti, Radu Sion [pdf] [bibtex]    

USENIX Security Symposium 2021  (USENIX Security 2021)


INFUSE: Invisible plausibly-deniable file system for NAND flash 

Chen Chen, Anrin Chakraborti, Radu Sion [pdf] [bibtex]    

Proceedings on Privacy Enhancing Technologies 2020 (PETS 2020)


PD-DM: An efficient locality-preserving block device mapper with plausible deniability

Chen Chen, Anrin Chakraborti, Radu Sion [pdf] [bibtex]

Proceedings on Privacy Enhancing Technologies 2019 (PETS 2019


DataLair: An Efficient Block Device Mapper with Plausible Deniability

Anrin Chakraborti, Chen Chen, Radu Sion [pdf] [bibtex]

Proceedings on Privacy Enhancing Technologies 2017 (PETS 2017


KXRay -- Introspecting the kernel for rootkit timing footprints

Chen Chen, Darius Suciu, Radu Sion [pdf] [bibtex]

23rd ACM Conference on Computer and Communications Security (CCS 2016 Poster


DataLair - A Storage Block Device with Plausible Deniability

Anrin Chakraborti, Chen Chen, Radu Sion [pdf] [bibtex]

23rd ACM Conference on Computer and Communications Security (CCS 2016 Poster


Quantitative Musings on the Feasibility of Smartphone Clouds

Chen Chen, Moussa Ehsan, Radu Sion [pdf]  [bibtex]

15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2015)


HIFS: History Independence for File Systems

Sumeet Bajaj, Chen Chen, Abhishek Kumar, Radu Sion [pdf] [bibtex]

24th ACM Symposium on Operating Systems Principles (SOSP 2013 Poster


Projects

Pearl: Plausibly Deniable Flash Translation Layer using WOM coding

PEARL is a plausibly deniable FTL for NAND flash memory. It allows both public data and hidden data to coexist in one flash device without leaking the existence of the hidden data to multi-snapshot adversaries. 

INFUSE: Plausible Deniable file system for NAND Flash Device 

INFUSE is a plausible deniability system for raw NAND flash devices. It hides sensitive data in flash devices by “embedding” logical data using variations in threshold voltage. The hidden data stored in the device and the deployment of INFUSE can be both “invisible” to adversaries. 

Plausible Deniability for Block Device  

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.