Doris

This repo shows the constructions of OXT [1], HXT[2], ConjFilter[3] and Doris[4].

Environment Configuration

• ubuntu20.04
• python3.8
• pypbc

  # install requirements libraries
  sudo apt update 
  sudo apt-get install flex bison libgmp-dev make
  
  # Download PBC source code and compile it
  cd ~ &&
  wget https://crypto.stanford.edu/pbc/files/pbc-0.5.14.tar.gz &&
  tar -xf pbc-0.5.14.tar.gz &&
  cd pbc-0.5.14 &&
  ./configure --prefix=/usr --enable-shared &&
  make &&
  sudo make install &&
  cd .. 
  
  # re-configure the ldconfig
  sudo ldconfig
  
  # Install the pypbc
  git clone https://github.com/debatem1/pypbc &&
  cd pypbc &&
  sudo pip3 install .
  cd ..
• install source code and additional python libraries

  git clone https://github.com/CDSecLab/Doris.git
  cd Doris
  pip3 install -r requirements.txt 

File Structure

.
├── README.md
├── requirements.txt
|
| // the constructions of OXT, HXT, ConjFilter and Doris
├── ConjFilter_alter.py // ConjFilter that support single keyword search
├── ConjFilter_ori.py // ConjFilter original scheme
├── Doris_XF.py
├── HXT.py
├── OXT.py
|
├── data // database of enron and enwiki, including indexes and inverted indexes
│   ├── enron_index0.csv // 10^2 key/value pairs
│   ├── enron_index1.csv // 10^3 key/value pairs
│   ├── enron_index2.csv // 10^4 key/value pairs
│   ├── enron_index3.csv // 10^5 key/value pairs
│   ├── enron_index4.csv // 10^6 key/value pairs
│   ├── enron_inverted0.csv
│   ├── enron_inverted1.csv
│   ├── enron_inverted2.csv
│   ├── enron_inverted3.csv
│   ├── enron_inverted4.csv
|   ├── ...
|
├── Utils 
│   ├── BF.py 
│   ├── SHVE.py 
│   ├── SSPE_XF.py
│   ├── TSet.py
│   ├── XorFilter.py
│   ├── cfg.py
│   ├── cryptoUtils.py
│   ├── fileUtils.py
│   ├── pbcUtils.py
│   └── test // examples and tests of tools
|
| // experiment
├── multi_keywords_exp.py
├── setup_exp.py
├── tools_exp.py
└── two_keywords_exp.py

Examples

Running the full experiment requires at least 128GB of memory, which is lightweight here, so you need to make sure you have at least 8GB of memory when running the experiment below.

protocol

python3 OXT.py
python3 HXT.py
python3 ConjFilter_alter.py
python3 Doris_XF.py

• Test whether the environment is configured successfully and run four protocols

tools experiment

python3 tools_exp.py

• This demo compares the storage and query overhead of the two tools
  • SHVE in HXT[2]: Utils/SHVE.py
  • SSPE in Doris[4]: Utils/SSPE_XF.py
• Log output to log/tools_enc_exp.log,log/tools_query_exp.log
• use about 5 min, 8 GB RAM
• If FileNotFoundError appears, create a folder log in the project root directory

setup experiment

python3 setup_exp.py

• The demo evaluates the mentioned schemes in terms of storage and computation cost at setup phase
• running on both large and small databases
  • enron database
  • enwiki database
• Log output to log/setup_enron_exp.log,log/setup_enwiki_exp.log
• use about 30 min, 8 GB RAM

two-keyword experiment

python3 two_keywords_exp.py

• The demo evaluates the computation cost and communication overhead of the four schemes for two keywords query
• Log output to log/two_keywords_exp.log
• use about 20 min (include the setup time and total query time)

multi-keyword experiment

python3 multi_keywords_exp.py

• Fix w1, change the number of queried keywords, and compare the computation cost and communication size of the four schemes
• Log output to log/multi_keywords_exp.log
• use about 50 min (include the setup time and total query time)

Contact Us

• Yunling Wang, [email protected]
• Jianfeng Wang, [email protected]

Reference

[1]. David Cash, Stanislaw Jarecki, Charanjit S. Jutla, Hugo Krawczyk, Marcel-Catalin Rosu, Michael Steiner: Highly-Scalable Searchable Symmetric Encryption with Support for Boolean Queries. CRYPTO 2013: 353-373.

[2]. Shangqi Lai, Sikhar Patranabis, Amin Sakzad, Joseph K. Liu, Debdeep Mukhopadhyay, Ron Steinfeld, Shifeng Sun, Dongxi Liu, Cong Zuo: Result Pattern Hiding Searchable Encryption for Conjunctive Queries. CCS 2018: 745-762.

[3]. Sarvar Patel, Giuseppe Persiano, Joon Young Seo, Kevin Yeo: Efficient Boolean Search over Encrypted Data with Reduced Leakage. ASIACRYPT 2021: 577-607.

[4]. Yunling Wang, Shi-Feng Sun, Jianfeng Wang, Xiaofeng Chen, Joseph K.Liu, and Dawu Gu. Practical Non-interactive Encrypted Conjunctive Search with Leakage Suppression. CCS 2024.