https://github.com/cBioLab/BiORAM-SGX

A Practical Privacy-Preserving Data Analysis for Personal Genome by Intel SGX.
https://github.com/cBioLab/BiORAM-SGX

bioinformatics oram ppdm sgx

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A Practical Privacy-Preserving Data Analysis for Personal Genome by Intel SGX.

• Host: GitHub
• URL: https://github.com/cBioLab/BiORAM-SGX
• Owner: cBioLab
• License: other
• Created: 2020-03-16T08:03:24.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-29T11:57:01.000Z (over 4 years ago)
• Last Synced: 2024-08-03T01:24:08.122Z (4 months ago)
• Topics: bioinformatics, oram, ppdm, sgx
• Language: C++
• Homepage:
• Size: 45 MB
• Stars: 9
• Watchers: 2
• Forks: 2
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-SGX-Open-Source - https://github.com/cBioLab/BiORAM-SGX

README

        
# BiORAM-SGX

A Practical Privacy-Preserving Data Analysis for Personal Genome by Intel SGX.

# Abstract

Intel SGX is a technology that can executes programs securely using Enclave, secure region on DRAM created by Intel's CPU. But, it is difficult to implement programs using Intel SGX. BiORAM-SGX enable to implement statistical analysis for personal genome data easily and flexibly using Intel SGX. 

In this system, when client request to analyze personal genome data, they get only result. During analysis, data do not leak to client and server, and the analysis procedures do not leak to the server. BiORAM-SGX deploys JavaScript interpreter on Enclave to analyze data flexibly and protect personal genome data. 

Interpreter has functions of statisical analysis for bioinformatics. Therefore, it is easy for client to imprement various kind of statistical programs. BiORAM-SGX stores personal genome data with encryption, and decrypt it only on Enclave. BiORAM-SGX uses [Path ORAM](https://dl.acm.org/doi/abs/10.1145/2508859.2516660) to get encrypted personal genome data quickly and securely.

+ Client: people who analyze personal genome data.

+ Data Owner: people who provide SGX Server with personal genome data.

+ SGX Server: server that has environment using Intel SGX. We assume that SGX Server is malicious.



# Demo

[![BiORAM-SGX](https://github.com/cBioLab/BiORAM-SGX/blob/master/figure/BiORAM-SGX_demo_top.png)](http://www.youtube.com/watch?v=SUXBFdLGHHA "BiORAM-SGX")

※ This demo movie is older than latest version of BiORAM-SGX. Therefore, some of implementation on this movie are a little different from latest specification.

# Installation Requirements

+ BiORAM-SGX needs "linux-sgx" and "linux-sgx-driver". Install them from following site.

    + [linux-sgx](https://github.com/intel/linux-sgx): ver. 2.5

    + [linux-sgx-driver](https://github.com/intel/linux-sgx-driver)

+ BiORAM-SGX also needs following libraries.

```

apt install sqlite3

apt install libsqlite3-dev

apt-get install libcurl4-openssl-dev

```

+ Run the following command to get your system's OpenSSL version. It must be at least 1.1.0:

```

openssl version

```

+ If necessary, download the source for the latest release of OpenSSL 1.1.0, then build and install it into a _non-system directory_ such as /opt (note that both `--prefix` and `--openssldir` should be set when building OpenSSL 1.1.0). For example:

```

wget https://www.openssl.org/source/openssl-1.1.0i.tar.gz

tar xf openssl-1.1.0i.tar.gz

cd openssl-1.1.0i

./config --prefix=/opt/openssl/1.1.0i --openssldir=/opt/openssl/1.1.0i

make

sudo make install

```

# Installation

```

cd ~

git clone [email protected]:cBioLab/BiORAM-SGX.git

cd BiORAM-SGX

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/BiORAM-SGX/sample_libcrypto

./bootstrap

./configure --with-openssldir=/opt/openssl/1.1.0i

make

mkdir SGXserver_data

cd SGXserver_data

mkdir upload_data

mkdir ORAM_table

```

+ You should get your service provider id(SPID) and Attestation Report Root CA Certificate(Intel_SGX_Attestation_RootCA.pem).

  + If you get SPID, write it on setting. Check [HERE](https://github.com/intel/sgx-ra-sample#building-the-sample) for detail.

  + Intel_SGX_Attestation_RootCA.pem can get following way.

  ```

  cd ~/BiORAM-SGX/

  wget https://certificates.trustedservices.intel.com/Intel_SGX_Attestation_RootCA.pem

  ```

  

+ If you have any problem, you should check [sgx-ra-sample](https://github.com/intel/sgx-ra-sample).

# Sample Running

## Create database for user verification

At first, create table on `~/BiORAM-SGX/`.

```

cd ~/BiORAM-SGX/

sqlite3 testdb

$ SQLite version x.xx.x 20xx-xx-xx xx:xx:xx

$ Enter ".help" for usage hints.

$ sqlite> create table users(id text, pwhash text);

$ sqlite> .exit

```

Then, register your id and pwhash.

```

cd ~/BiORAM-SGX/

python3 CreateID_pass.py

$ Input userID:   DataOwner

$ Input password: DataOwner

$ Are you sure to register this userID and password[y/n]?: y

python3 CreateID_pass.py

$ Input userID:   Client

$ Input password: Client

$ Are you sure to register this userID and password[y/n]?: y

```

## [Data Owner] Download genome data (1000 genome project)

```

cd ~/BiORAM-SGX/dataowner_data/

wget ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502/ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz

gunzip ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz

```

## [Data Owner] Split and Encrypt genome data

```

cd ~/BiORAM-SGX/dataowner_data/

# Split genome data by nation. Use "xlrd" library.

python SplitVCFData_nation.py 22

# Split nation genome data by each size(102000[byte]: about 100000 byte + padding).

python3 SplitVCFData_size.py ~/BiORAM-SGX/dataowner_data/ ~/BiORAM-SGX/dataowner_data/chr22_GWD/ 22 GWD 100000 2000

python3 SplitVCFData_size.py ~/BiORAM-SGX/dataowner_data/ ~/BiORAM-SGX/dataowner_data/chr22_JPT/ 22 JPT 100000 2000

# Encrypt splitted nation genome data. We use Intel SGX for encryption, but it is not necessary for Data Owner to use Intel SGX in case Data Onwer encrypt them using AES-GCM.

cd EncryptAES_SGX

make

# GWD: Gambian in Western Division, The Gambia

# JPT: Japanese in Tokyo, Japan

./app ~/BiORAM-SGX/dataowner_data/chr22_GWD/ 22 GWD 102000

./app ~/BiORAM-SGX/dataowner_data/chr22_JPT/ 22 JPT 102000

cd ../

cp -r chr22_GWD chr22_JPT ../SGXserver_data/upload_data/

rm ../SGXserver_data/upload_data/chr22_GWD/AES_SK.key

rm ../SGXserver_data/upload_data/chr22_JPT/AES_SK.key

```

## ※ Shortcut for download, split and encrypt genome data.

Above commands take about 10 minutes because genome data of chromosome 22 is huge. If you use following commands, reduce time.

```

cd ~/BiORAM-SGX/dataowner_data/

# short size of genome data.

gunzip *.gz

python3 SplitVCFData_size.py ~/BiORAM-SGX/dataowner_data/ ~/BiORAM-SGX/dataowner_data/chr22_GWD/ 22 GWD 100000 2000

python3 SplitVCFData_size.py ~/BiORAM-SGX/dataowner_data/ ~/BiORAM-SGX/dataowner_data/chr22_JPT/ 22 JPT 100000 2000

cd EncryptAES_SGX

make

./app ~/BiORAM-SGX/dataowner_data/chr22_GWD/ 22 GWD 102000

./app ~/BiORAM-SGX/dataowner_data/chr22_JPT/ 22 JPT 102000

cd ../

cp -r chr22_GWD chr22_JPT ../SGXserver_data/upload_data/

rm ../SGXserver_data/upload_data/chr22_GWD/AES_SK.key

rm ../SGXserver_data/upload_data/chr22_JPT/AES_SK.key

```

## [Data Owner] Create ORAM structure

+ SGX Server side

```

./run-SGXserver

```

+ Data Owner side

```

./run-client

$ Input your user ID: DataOwner

$ Input your ID's password: DataOwner

$ (If you do not have key, push ENTER only.)

$ Input your SK filename: ./dataowner_data/chr22_GWD/AES_SK.key

$ Input your JavaScript code: ./dataowner_data/ORAMinit_GWD.js

---

./run-client

$ Input your user ID: DataOwner

$ Input your ID's password: DataOwner

$ (If you do not have key, push ENTER only.)

$ Input your SK filename: ./dataowner_data/chr22_JPT/AES_SK.key

$ Input your JavaScript code: ./dataowner_data/ORAMinit_JPT.js

```

## [Client] Analyze genome data

+ SGX Server side

```

./run-SGXserver

```

+ Client side

```

./run-client

$ Input your user ID: Client

$ Input your ID's password: Client

$ (If you do not have key, push ENTER only.)

$ Input your SK filename: [ENTER]

$ Input your JavaScript code: ./client_data/fisher.js

```

Client sample .js codes are as follows.

+ fisher.js: sample code to execute fisher's exact test.

+ LR.js:     sample code to execute logistic regression(100 positions).

+ PCA.js:    sample code to execute PCA(100 positions -> 2 dimension).

+ LR_PCA.js: execute LR(10 positions) -> select 5 positions that have high relation between GWD and JPT -> PCA(5 positions -> 2 dimension) -> save result as file.  

It can visualize as follows. Because sample positions are quite a few, classification is not proper.(If you check proper classification, see [demo](https://github.com/cBioLab/BiORAM-SGX#demo).) 

  ```

  cd ~/BiORAM-SGX/client_data/

  python Visualize_data.py

  ```

# Benchmark(2020/02/20)

## Machine Spec

+ OS: Ubuntu 18.04.3 LTS

+ CPU: Intel Core i7-7700K CPU @ 4.20GHz

+ memory: 16GB

+ [Intel SGX for Linux*](https://github.com/intel/linux-sgx/) 2.5, [Intel SGX Linux* Driver](https://github.com/intel/linux-sgx-driver/) 2.5

## Parameters

+ Z(see detail on [Path ORAM](https://dl.acm.org/doi/abs/10.1145/2508859.2516660) paper.): 6

+ StackMaxSize: 4[MB] (4,000,000 byte)

+ HeapMaxSize: 96[MB] (96,000,000 byte)

+ Data: 1000 Genome Project data, espwcially 2 nations.

  + GWD: Gambian in Western Division, The Gambia

  + JPT: Japanese in Tokyo, Japan

Genome data size are as follows.

|                      | AllGenome(JPT) | AllGenome(GWD) | chr1(JPT) | chr1(GWD) | chr22(JPT) | chr22(GWD) |

|:--------------------:|:--------------:|:--------------:|:---------:|:---------:|:----------:|:----------:|

|    Data size [GB]    |      35.8      |      38.6      |    2.76   |    2.97   |    0.471   |    0.508   |

| num of splitted data |     384758     |     415536     |   29658   |   32006   |    5062    |    5463    |

## Case1: AllGenome

We create ORAM Trees using all human chromosome, each nation(JPT, GWD).

+ Fisher

|   process   | time [sec] |

|:-----------:|:----------:|

| File Search |  4.372849  |

|   Analyze   |  0.0273248 |

|    Total    |  4.401838  |

+ LR  

Using gradient descent, regularization.

  

    

    number of positions

  

  

    10

    50

    100

  

  

    Fille Search [sec]

    47.97443

    216.4722

    406.3569

  

  

    Analyze [sec]

    0.0052505

    0.022678

    0.04415015

  

  

    Total [sec]

    47.98099

    216.4971

    406.40365

  

+ PCA  

In PCA, we use only JPT data, using power method.

  

    

    number of positions

  

  

    10

    50

    100

  

  

    Fille Search [sec]

    19.74556

    101.20553

    237.0048

  

  

    Analyze [sec]

    0.0002727

    0.0028131

    0.0117333

  

  

    Total [sec]

    19.74735

    101.21001

    237.0183

  

## Case2: chromosome 1

We create ORAM Trees using chromosome 1, each nation(JPT, GWD).

+ Fisher

|   process   | time [sec] |

|:-----------:|:----------:|

| File Search |  1.4665754 |

|   Analyze   |  0.0001375 |

|    Total    |  1.4682056 |

+ LR  

Using gradient descent, regularization.

  

    

    number of positions

  

  

    10

    50

    100

  

  

    Fille Search [sec]

    5.742125

    28.30003

    64.83146

  

  

    Analyze [sec]

    0.0055113

    0.022171

    0.0434385

  

  

    Total [sec]

    5.748933

    28.32372

    64.87664

  

+ PCA  

In PCA, we use only JPT data, using power method.

  

    

    number of positions

  

  

    10

    50

    100

  

  

    Fille Search [sec]

    2.47331

    13.19456

    27.24546

  

  

    Analyze [sec]

    0.006414

    0.0059026

    0.0153582

  

  

    Total [sec]

    2.475577

    13.20257

    27.26291

  

## Case3: chromosome 22

We create ORAM Trees using chromosome 22, each nation(JPT, GWD).

+ Fisher

|   process   | time [sec] |

|:-----------:|:----------:|

| File Search |  0.2158026 |

|   Analyze   |  0.0274049 |

|    Total    |  0.244528  |

+ LR  

Using gradient descent, regularization.

  

    

    number of positions

  

  

    10

    50

    100

  

  

    Fille Search [sec]

    3.184544

    22.78428

    39.85593

  

  

    Analyze [sec]

    0.0060702

    0.0235689

    0.0479591

  

  

    Total [sec]

    3.191978

    22.80935

    39.90606

  

+ PCA  

In PCA, we use only JPT data, using power method.

  

    

    number of positions

  

  

    10

    50

    100

  

  

    Fille Search [sec]

    1.470165

    9.026763

    15.40194

  

  

    Analyze [sec]

    0.0006192

    0.0039763

    0.0133208

  

  

    Total [sec]

    1.472607

    9.032648

    15.41728

  

# License

BiORAM-SGX is released under the [MIT License](https://opensource.org/licenses/mit-license). See [LICENSE](https://github.com/cBioLab/BiORAM-SGX/blob/master/LICENSE) for details.

Licenses of external libraries are listed as follows.

+ [linux-sgx](https://github.com/intel/linux-sgx/blob/master/License.txt)

+ [linux-sgx-driver](https://github.com/intel/linux-sgx-driver/blob/master/License.txt)

+ [OpenSSL](https://www.openssl.org/source/license.html)

+ [SQLite](https://www.sqlite.org/copyright.html)

+ [tiny-js](https://github.com/gfwilliams/tiny-js/blob/master/LICENSE)

# Acknowledgement

We thank Mr.Ao Sakurai for fruitful discussions.

# Contact

Daiki Iwata([email protected])