GuardianMPC

A Backdoor-resilient Deep Learning Accelerator

Description

Advancements in deep learning (DL) have unlocked unprecedented capabilities in various domains. However, alongside these advancements, the susceptibility of DL models to backdoor insertion attacks has become a significant concern. These attacks mimic the actions of a malicious adversary and can remain undetected under conventional security paradigms. MPC’s interactive computation approach aligns well with the need to counter backdoor attacks. In this work, we aim to build a bridge between MPC, and particularly, secure and private two-party computation, and the countermeasures against backdoor attacks. In doing so, we introduce GurdianMPC, a DL accelerator that employ the LEGO family of protocols to provide not only security against backdoor attacks, but also users with data privacy. Moreover, by leveraging optimization techniques tailored toward hardware acceleration, GurdianMPC achieves high efficiency and low latency. Specifically, our implementation on Field Programmable Gate Arrays (FPGAs) computational power evaluate a typical NN up to 13.44× faster during the online phase compared to its counterpart running on software, with a trade-off against a negligible offline computation time.

Dependencies:

Install dependencies on Ubuntu: g++:

 $ sudo apt-get install g++

OpenSSL:

  $ sudo apt-get install libssl-dev

boost:

  $ sudo apt-get install libboost-all-dev

cmake:

  $ sudo apt-get install software-properties-common
  $ sudo add-apt-repository ppa:george-edison55/cmake-3.x
  $ sudo apt-get update
  $ sudo apt-get upgrade
  $ sudo apt-get install cmake

TinyGarble:

  $ cd TintGarbe 
  $./configure
  $ cd bin
  $ make

ARM2GC:

  $ sudo apt install binutils-arm-linux-gnueabi
  $ sudo apt install gcc-arm-linux-gnueabi

TinyGarble2:

  $ git clone https://github.com/IntelLabs/TinyGarble2.0.git
  $ git clone https://github.com/IntelLabs/emp-tool.git
  $ git clone https://github.com/IntelLabs/emp-ot.git
  $ sudo ./TinyGarble2.0/install_scripts/install_dependencies.sh
  $ cd emp-tool
  $ cmake . -DCMAKE_INSTALL_PREFIX=<install_path>
  $ make -j 
  $ make install -j
  $ cd ..
  $ cd emp-ot
  $ cmake . -DCMAKE_INSTALL_PREFIX=<install_path>
  $ make -j 
  $ make install -j 
  $ cd ..
  $ cd TinyGarble2.0
  $ cmake . -DCMAKE_INSTALL_PREFIX=<install_path>
  $ make -j 
  $ make install -j

References

• [1] M. Hashemi, S. Roy, D. Forte, F. Gangi, “HWGN2: Side-Channel Protected NNs Through Secure and Private Function Evaluation,” Security, Privacy, and Applied Cryptography Engineering (SPACE22), vol. 13783, pp. 225-248, 2022. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-031-22829-2_13.

• [2] T. K. Frederiksen, T. P. Jakobsen, J. B. Nielsen, R. Trifiletti, “TinyLEGO: An Interactive Garbling Scheme for Maliciously Secure Two-Party Computation,” IACR Cryptology ePrint Archive, vol. 2015, p. 309, 2015. [Online]. Available: http://ia.cr/2015/309.

• [3] J. B. Nielsen, T. Schneider, R. Trifiletti, "Constant Round Maliciously Secure 2PC with Function-independent Preprocessing using LEGO", in The Network and Distributed System Security Symposium (NDSS) 2017. Availible: http://ia.cr/2016/1069.