Skip to content
master
Go to file
Code

Latest commit

* Support for multiple branched CaseWhen

* Remove print statements

* Support optional else statement and add tests

* Add tests for CaseWhen

* Fix test cases

* Add support for LIKE expression

* Fix typo and remove vector copying

* Add SQL API test

Co-authored-by: Andrew Law <andrewlaw@sharkfin.local>
6e0d1c9

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Opaque

Secure Apache Spark SQL

Build Status

Opaque is a package for Apache Spark SQL that enables encryption for DataFrames using the OpenEnclave framework. The aim is to enable analytics on sensitive data in an untrusted cloud. Once the contents of a DataFrame are encrypted, subsequent operations will run within hardware enclaves (such as Intel SGX).

This project is based on the following NSDI 2017 paper [1]. The oblivious execution mode is not included in this release.

This is an alpha preview of Opaque, which means the software is still in development (not production-ready!). It currently has the following limitations:

  • Unlike the Spark cluster, the master must be run within a trusted environment (e.g., on the client).

  • Not all Spark SQL operations are supported. UDFs must be implemented in C++.

  • Computation integrity verification (section 4.2 of the NSDI paper) is currently work in progress.

[1] Wenting Zheng, Ankur Dave, Jethro Beekman, Raluca Ada Popa, Joseph Gonzalez, and Ion Stoica. Opaque: An Oblivious and Encrypted Distributed Analytics Platform. NSDI 2017, March 2017.

Installation

After downloading the Opaque codebase, build and test it as follows.

  1. Install dependencies and the OpenEnclave SDK. We currently support OE version 0.9.0 (so please install with open-enclave=0.9.0) and Ubuntu 18.04.

    # For Ubuntu 18.04:
    sudo apt install wget build-essential openjdk-8-jdk python libssl-dev
    
    # Install a newer version of CMake (>= 3.13)
    wget https://github.com/Kitware/CMake/releases/download/v3.15.6/cmake-3.15.6-Linux-x86_64.sh
    sudo bash cmake-3.15.6-Linux-x86_64.sh --skip-license --prefix=/usr/local
  2. On the master, generate a keypair using OpenSSL for remote attestation.

    openssl genrsa -out private_key.pem -3 3072
  3. Change into the Opaque root directory and edit Opaque's environment variables in opaqueenv if desired. Export Opaque and OpenEnclave environment variables via

    source opaqueenv
    source /opt/openenclave/share/openenclave/openenclaverc

    By default, Opaque runs in hardware mode (environment variable MODE=HARDWARE). If you do not have a machine with a hardware enclave but still wish to test out Opaque's functionality locally, then set export MODE=SIMULATE.

  4. Run the Opaque tests:

    cd ${OPAQUE_HOME}
    build/sbt test

Usage

Next, run Apache Spark SQL queries with Opaque as follows, assuming Spark 3.0 (wget http://apache.mirrors.pair.com/spark/spark-3.0.1/spark-3.0.1-bin-hadoop2.7.tgz) is already installed:

  1. Package Opaque into a JAR:

    cd ${OPAQUE_HOME}
    build/sbt package
  2. Launch the Spark shell with Opaque:

    ${SPARK_HOME}/bin/spark-shell --jars ${OPAQUE_HOME}/target/scala-2.12/opaque_2.12-0.1.jar

    Alternatively, to run Opaque queries locally for development rather than on a cluster:

    cd ${OPAQUE_HOME}
    JVM_OPTS="-Xmx4G" build/sbt console
  3. Inside the Spark shell, import Opaque's DataFrame methods and install Opaque's query planner rules:

    import edu.berkeley.cs.rise.opaque.implicits._
    
    edu.berkeley.cs.rise.opaque.Utils.initSQLContext(spark.sqlContext)
  4. Create an encrypted DataFrame:

    val data = Seq(("foo", 4), ("bar", 1), ("baz", 5))
    val df = spark.createDataFrame(data).toDF("word", "count")
    val dfEncrypted = df.encrypted
  5. Query the DataFrames and explain the query plan to see the secure operators:

    dfEncrypted.filter($"count" > lit(3)).explain(true)
    // [...]
    // == Optimized Logical Plan ==
    // EncryptedFilter (count#6 > 3)
    // +- EncryptedLocalRelation [word#5, count#6]
    // [...]
    
    dfEncrypted.filter($"count" > lit(3)).show
    // +----+-----+
    // |word|count|
    // +----+-----+
    // | foo|    4|
    // | baz|    5|
    // +----+-----+
  6. Save and load an encrypted DataFrame:

    dfEncrypted.write.format("edu.berkeley.cs.rise.opaque.EncryptedSource").save("dfEncrypted")
    // The file dfEncrypted/part-00000 now contains encrypted data
    
    import org.apache.spark.sql.types._
    val df2 = (spark.read.format("edu.berkeley.cs.rise.opaque.EncryptedSource")
      .schema(StructType(Seq(StructField("word", StringType), StructField("count", IntegerType))))
      .load("dfEncrypted"))
    df2.show
    // +----+-----+
    // |word|count|
    // +----+-----+
    // | foo|    4|
    // | bar|    1|
    // | baz|    5|
    // +----+-----+

User-Defined Functions (UDFs)

To run a Spark SQL UDF within Opaque enclaves, first name it explicitly and define it in Scala, then reimplement it in C++ against Opaque's serialized row representation.

For example, suppose we wish to implement a UDF called dot, which computes the dot product of two double arrays (Array[Double]). We define it in Scala in terms of the Breeze linear algebra library's implementation. We can then use it in a DataFrame query, such as logistic regression.

Now we can port this UDF to Opaque as follows:

  1. Define a corresponding expression using Opaque's expression serialization format by adding the following to Expr.fbs, which indicates that a DotProduct expression takes two inputs (the two double arrays):

    table DotProduct {
        left:Expr;
        right:Expr;
    }

    In the same file, add DotProduct to the list of expressions in ExprUnion.

  2. Implement the serialization logic from the Scala DotProduct UDF to the Opaque expression that we just defined. In Utils.flatbuffersSerializeExpression, add a case for DotProduct as follows:

    case (DotProduct(left, right), Seq(leftOffset, rightOffset)) =>
      tuix.Expr.createExpr(
        builder,
        tuix.ExprUnion.DotProduct,
        tuix.DotProduct.createDotProduct(
          builder, leftOffset, rightOffset))
  3. Finally, implement the UDF in C++. In FlatbuffersExpressionEvaluator#eval_helper, add a case for tuix::ExprUnion_DotProduct. Within that case, cast the expression to a tuix::DotProduct, recursively evaluate the left and right children, perform the dot product computation on them, and construct a DoubleField containing the result.

Contact

If you want to know more about our project or have questions, please contact Wenting (wzheng@eecs.berkeley.edu) and/or Ankur (ankurdave@gmail.com).

You can’t perform that action at this time.