Integrity docs

docs/src/integrity/integrity.rst

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+***********************
+Computational Integrity
+***********************
+
+The integrity module of Opaque ensures that the untrusted job driver hosted on the cloud service schedules tasks in the manner computed by Spark's Catalyst query optimizer. 
+Opaque runs on Spark, which utilizes data partitioning to speed up computation. 
+Specifically, Catalyst will compute a physical query plan for a given dataframe query and delegate Spark workers (run on enclaves) to compute Spark SQL operations on data partitions. 
+Each of these individual units is trusted, but the intermediary steps in which the units communicate is controlled by the job driver, running as untrusted code in the cloud. 
+The integrity module will detect if the job driver has deviated from the query plan computed by Catalyst.
+
+Overview
+--------
+The main idea behind integrity support is to tag each step of computation with a MAC, attached by the enclave worker when it has completed its computation. 
+All MACs received by all previous enclave workers are logged. In the end, these MACs are compared and reconstructed into a graph. 
+This graph is compared to that computed by Catalyst. 
+If the graphs are isomorphic, then no tampering has occurred. 
+Else, the result of the query returned by the cloud is rejected.
+
+Implementation
+--------------
+Two main extensions were made to support integrity - one in enclave code, and one in the Scala client application.
+
+Enclave Code
+^^^^^^^^^^^^
+In the enclave code (C++), modifications were made to the ``FlatbuffersWriters.cpp`` file. 
+Attached to every output of an ``EncryptedBlocks``` object is a MAC over the output.
+No further modifications need to be made to the application logic since this functionality hooks into how Opaque workers output their data.
+
+Scala/Application Code
+^^^^^^^^^^^^^^^^^^^^^^
+The main extension supporting Integrity is the ```JobVerificationEngine`` which is a piece of Scala code that broadly carries out three tasks:
+
+1. Reconstruct the flow of information between enclave workers.
+
+2. Compute the corresponding DAG of ecalls for a given query.
+
+3. Compare the two DAGs and output "accept" or "reject."
+
+These happen in the "verify" function of the JobVerificationEngine class.
+
+Reconstructing the executed DAG of ecalls involves iterating through the MACs attached by enclave workers, provided in the "LogEntryChain" object in the Job Verification Engine.
+This object is filled by Opaque when Spark's ``collect`` method is called when a query is executed.
+
+Output MACs of parents correspond to input MACs of their child. Using this information, the DAG is created.
+
+The "expected" DAG is created from Spark's ``dataframe.queryPlan.executedPlan`` object which is a recursive tree node of Spark Operators.
+The Job Verification Engine contains the logic to transform this tree of operators into a tree of ecalls.
+
+Adding Integrity Support for New Operators
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+To support new operators, if they are added, one should make changes to the Enclave code and the Job Verification Engine code.
+
+In the enclave, make sure that the enclave context's "finish_ecall" method is called before returning in ``Enclave.cpp```.
+
+In the Job Verification Engine, add the logic to transform the operator into a list of ecalls that the operator uses in ``generateJobNodes``.
+This amounts to adding a case in the switch statement of this function.
+
+Furthermore, add the logic to connect the ecalls together in ``linkEcalls``.
+As above, this amounts to adding a case in the switch statement of this function, but requires knowledge of how each ecall communicates the transfer of data partitions to its successor ecall
+(broadcast, all to one, one to all, etc.).