Carbyne Stack Klyshko Correlated Randomness Generation

Klyshko is a kubernetes-native open source correlated randomness generator (CRG) service for Secure Multiparty Computation in the offline/online model and part of Carbyne Stack.

DISCLAIMER: Carbyne Stack Klyshko is in proof-of-concept stage. The software is not ready for production use. It has neither been developed nor tested for a specific use case.

Namesake

Klyshko is one of the inventors of spontaneous parametric down-conversion (SPDC). SPDC is an important process in quantum optics, used especially as a source of entangled photon pairs, and of single photons (see Wikipedia). The analogy to the Klyshko service is that secret shared tuples are correlated and thus kind of "entangled" and that the microservice is the implementation of the process that creates the tuples.

Architecture

Klyshko consists of three main components:

• Correlated Randomness Generators (CRGs) (kind: TupleGenerator) are the workhorses within Klyshko. They are actually generating correlated randomness. CRGs are packaged as Docker images and have to implement the Klyshko Integration Interface (KII).
• The Klyshko Operator coordinates the invocation of CRGs across the VCPs in a VC. It consists of a number of components implemented as a Kubernetes API called klyshko.carbnyestack.io/v1alpha1 providing the following Custom Resources:
  • A Scheduler (kind: TupleGenerationScheduler) monitors the availability of correlated randomness within the VC using the Castor Telemetry API and schedules CRG invocations accordingly.
  • A Job (kind: TupleGenerationJob) abstracts a CRG invocation across the VCPs of a VC. The job holds the specification of the correlated randomness to be generated including tuple type and the number of tuples to be generated.
  • A Task (kind: TupleGenerationTask) represents a local or remote execution of a CRG. A task exposes the state of the invocation on a single VCP. On the job level, task states are aggregated into a job state. Remote tasks are proxied locally to make their state available to the job controller. The task controller makes use of the Klyshko Integration Interface (KII) to interact with different CRG implementations in an implementation-independent way.
• The Klyshko Provisioner is used to upload the generated correlated randomness to Castor.

Klyshko uses an etcd cluster to manage distributed state and to orchestrate actions across VCPs.

Usage

To deploy Klyshko to your VC you have to perform the following steps:

Provide VCP Configuration

NOTE: This is a workaround until the Carbyne Stack Operator (see CSEP-0053) is available.

Klyshko needs to know the overall number of VCPs in the VC and the zero-based index of the local VCP. This done by creating a configuration map with the following content:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cs-vcp-config
data:
  playerCount: <<NUMBER-OF-VCPS>>
  playerId: <<ZERO-BASED-INDEX-OF-LOCAL-VCP>>

Install the operator

The Klyshko operator can be deployed either by building from source or by using helm. Both variants are described below. Remember to perform the respective steps on all VCPs of your VC.

From Source

You can use the make tool to build and deploy the operator using

cd klyshko-operator
make deploy IMG="carbynestack/klyshko-operator:v0.3.0"

Using Helm

You can deploy the Klyshko operator using helm as follows:

HELM_EXPERIMENTAL_OCI=1 helm install klyshko \
  oci://ghcr.io/carbynestack/klyshko \
  --version 0.3.0

Provide the Configuration

Klyshko requires CRG-specific configuration that is provided via K8s config maps and secrets (see here for details). Consult the documentation of the MP-SPDZ CRG for information of what has to be provided.

Registering a Tuple Generator

After configuration is done, you can create a Tuple Generator using, e.g.,

cat <<EOF | kubectl apply -f -
apiVersion: klyshko.carbnyestack.io/v1alpha1
kind: TupleGenerator
metadata:
  name: mp-spdz-fake
spec:
  template:
    spec:
      container:
        image: carbynestack/klyshko-mp-spdz:0.2.0
  supports:
    - type: BIT_GFP
      batchSize: 100000
    - type: INPUT_MASK_GFP
      batchSize: 100000
    - type: INVERSE_TUPLE_GFP
      batchSize: 100000
    - type: SQUARE_TUPLE_GFP
      batchSize: 100000
    - type: MULTIPLICATION_TRIPLE_GFP
      batchSize: 100000
    - type: BIT_GF2N
      batchSize: 100000
    - type: INPUT_MASK_GF2N
      batchSize: 100000
    - type: INVERSE_TUPLE_GF2N
      batchSize: 100000
    - type: SQUARE_TUPLE_GF2N
      batchSize: 100000
    - type: MULTIPLICATION_TRIPLE_GF2N
      batchSize: 100000
EOF

This registers the generator with Klyshko. Note that you have to specify each tuple type supported by the CRG and provide a recommended batch size for jobs that generate that type of tuples. Please consult the CRG documentation for more information.

IMPORTANT: In case a tuple type is supported by multiple generators no tuples are generated for that tuple type to avoid potential inconsistencies across VCPs.

CRG Pod Template

You can customize some aspects of the pod launched for a tuple generation task, i.e., the pod that hosts the container running the generator image. The following fields are customizable (in lexical order):

Aspect	Description	Field(s)
Affinity	Used to constrain on which nodes the generator pod can run (see here for details).	spec.template.spec.affinity
Image	The generator image to use (see here for details).	spec.template.spec.container.{image,imagePullPolicy}
Resources	How much resources the container needs (see here for details).	spec.template.spec.container.resources

Note that spec.template.spec.container.image is the only mandatory field. If a field is not provided the general default values for pods / containers are used (see links provided above).

A fully customized sample generator pod template looks like the following:

apiVersion: klyshko.carbnyestack.io/v1alpha1
kind: TupleGenerator
metadata:
  name: mp-spdz-fake
spec:
  template:
    spec:
      affinity: # Only place pod on nodes running a Linux OS
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: kubernetes.io/os
                  operator: In
                  values:
                    - linux
      container:
        image: carbynestack/klyshko-mp-spdz:0.2.0
        imagePullPolicy: Always
        resources:
          requests: # Asking for 2 GB of memory and 1 CPU unit (physical or virtual CPU core)
            memory: "2G"
            cpu: "1"
  supports:
    - ...

Instantiating a Scheduler

After configuration is done, you create a scheduler on one of the clusters by applying the respective manifest, e.g.,

apiVersion: klyshko.carbnyestack.io/v1alpha1
kind: TupleGenerationScheduler
metadata:
  name: sample-crg-scheduler
spec:
  concurrency: 3
  policies:
    - type: BIT_GFP
      threshold: 1000000
    - type: INPUT_MASK_GFP
      threshold: 1000000
      priority: 10
    - type: INVERSE_TUPLE_GFP
      threshold: 1000000
    - type: SQUARE_TUPLE_GFP
      threshold: 1000000
    - type: MULTIPLICATION_TRIPLE_GFP
      threshold: 1000000
      priority: 10
    - type: BIT_GF2N
      threshold: 100000
    - type: INPUT_MASK_GF2N
      threshold: 100000
    - type: INVERSE_TUPLE_GF2N
      threshold: 100000
    - type: SQUARE_TUPLE_GF2N
      threshold: 100000
    - type: MULTIPLICATION_TRIPLE_GF2N
      threshold: 100000

Klyshko will start producing correlated randomness for the given tuple types according to the respective policy. Klyshko will run CRGs in parallel as specified by the concurrency parameter. If the number of running jobs drops below that number, Klyshko selects the next tuple type to launch a job for using a lottery scheduler. The number of tickets assigned to a tuple type is specified by the optional priority parameter (default is 1 when not given). Only those tuple types for which less than threshold number of tuples are available in Castor are eligible for scheduling.

Klyshko Integration Interface (KII)

IMPORTANT: This is an initial incomplete version of the KII that is subject to change without notice. For the time being it is very much influenced by the CRGs provided as part of the MP-SPDZ project.

Klyshko has been designed to allow for easy integration of different Correlated Randomness Generators (CRGs). Integration is done by means of providing a docker image containing the CRG that implements the Klyshko Integration Interface (KII). The parameters required by the CRG are provided using a mix of environment variables and files made available to the container during execution. See below for a detailed description.

TIP: For an example of how to integrate the MP-SPDZ CRG producing fake tuples see the klyshko-mp-spdz module.

Entrypoint

The CRG docker image must spawn the tuple generation process when launched as a container. The command given as the entrypoint must terminate with a non-zero exit code if and only if the tuples could not be generated for some reason.

Environment Variables

The following environment variables are passed into CRG containers to control the tuple generation and provisioning process.

Input

• KII_JOB_ID: The Type 4 UUID used as a job identifier. This is the same among all VCPs in the VC.
• KII_TUPLES_PER_JOB: The number of tuples to be generated. The CRG should make its best effort to match the requested number but is not required to do so in case optimizations like batching mandate it.
• KII_PLAYER_NUMBER: The 0-based number of the local VCP.
• KII_PLAYER_COUNT: The overall number of VCPs in the VC.
• KII_TUPLE_TYPE: The tuple type to generate. Must be one of
  • BIT_GFP, BIT_GF2N
  • INPUT_MASK_GFP, INPUT_MASK_GF2N
  • INVERSE_TUPLE_GFP, INVERSE_TUPLE_GF2N
  • SQUARE_TUPLE_GFP, SQUARE_TUPLE_GF2N
  • MULTIPLICATION_TRIPLE_GFP, MULTIPLICATION_TRIPLE_GF2N

Output

• KII_TUPLE_FILE: The file the generated tuples must be written to.

Configuration Parameters

CRGs typically require some configuration that has to be provided using K8s config maps and secrets. While the existence of these resources is dictated by the KII, their content is CRG implementation specific. Please refer to the CRG documentation for detailed information on what is expected. The following examples are for the MP-SPDZ CRG.

Public Parameters

Public, i.e., non-sensitive, parameters are provided in a config map with name io.carbynestack.engine.params as follows:

apiVersion: v1
kind: ConfigMap
metadata:
  name: io.carbynestack.engine.params
data:
  prime: <<PRIME>>

They are provided to CRGs as files in the /etc/kii/params/ folder. The file /etc/kii/params/prime contains the <<PRIME>> in the example above.

Secret Parameters

Sensitive parameters are provided using a K8s secret with name io.carbynestack.engine.params.secret as follows:

apiVersion: v1
kind: Secret
metadata:
  name: io.carbynestack.engine.params.secret
type: Opaque
data:
  mac_key_share_p: |
    <<MAC_KEY_SHARE_P>>
  mac_key_share_2: |
    <<MAC_KEY_SHARE_2>>

They are made available to CRGs as files in the folder /etc/kii/secret-params.

Additional Parameters

Additional parameters may be provided using a K8s config map with name io.carbynestack.engine.params.extra as follows:

apiVersion: v1
kind: ConfigMap
metadata:
  name: io.carbynestack.engine.params.extra
data:
  <<KEY-#1>>: <<VALUE-#1>>
  <<KEY-#2>>: <<VALUE-#2>>

These are made available to CRGs by the Klyshko runtime as files in folder /etc/kii/extra-params. For an example of how this is used see the MP-SPDZ fake tuple CRG.

Development

SDK-based

The recommended and future-proof way to deploy Klyshko during development is by means of the Carbynestack SDK. You can customize the deployment logic to use your local Klyshko chart and images as follows:

1. Build the Klyshko docker images locally using

   export VERSION=local-dev
   
   # Build the MP-SPDZ fake CRG image
   pushd klyshko-mp-spdz
   docker build -f Dockerfile.fake-offline . -t "ghcr.io/carbynestack/klyshko-mp-spdz:${VERSION}"
   popd
   
   # and the MP-SDPZ CowGear CRG image
   pushd klyshko-mp-spdz-cowgear
   docker build -f Dockerfile . -t "ghcr.io/carbynestack/klyshko-mp-spdz-cowgear:${VERSION}"
   popd
   
   # Build the Provisioner image
   pushd klyshko-provisioner
   docker build -f Dockerfile . -t "ghcr.io/carbynestack/klyshko-provisioner:${VERSION}"
   popd
   
   # Build the Operator image
   pushd klyshko-operator
   make docker-build IMG="ghcr.io/carbynestack/klyshko-operator:${VERSION}"
   popd

2. Make the SDK locally available by cloning the carbynestack/carbynestack repository

   git clone [email protected]:carbynestack/carbynestack.git sdk
   cd sdk

3. Load the generated docker images into your kind clusters using

   declare -a CLUSTERS=("starbuck" "apollo")
   for c in "${CLUSTERS[@]}"
   do
     kind load docker-image \
       "ghcr.io/carbynestack/klyshko-mp-spdz:${VERSION}" \
       "ghcr.io/carbynestack/klyshko-mp-spdz-cowgear:${VERSION}" \
       "ghcr.io/carbynestack/klyshko-provisioner:${VERSION}" \
       "ghcr.io/carbynestack/klyshko-operator:${VERSION}" \
       --name "$c"
   done

4. Update the Klyhsko helm chart in the SDK to use your local chart by substituting the line

   ...
   chart: carbynestack-oci/klyshko
   ...

   with

   ...
   chart: <YOUR_KLYSHKO_REPOSITORY_ROOT>/klyshko-operator/charts/klyshko
   ...

   in the file <YOUR_SDK_REPOSITORY_ROOT>/helmfile.d/0400.klyshko.yaml.

5. Deploy Carbyne Stack with your locally build artifacts via

   # Overwrite Klyshko images used for deployment
   export KLYSHKO_GENERATOR_IMAGE_TAG="${VERSION}"
   export KLYSHKO_PROVISIONER_IMAGE_TAG="${VERSION}"
   export KLYSHKO_OPERATOR_IMAGE_TAG="${VERSION}"
   
   # Configure deployment
   export APOLLO_FQDN="172.18.1.128.sslip.io"
   export STARBUCK_FQDN="172.18.2.128.sslip.io"
   export RELEASE_NAME=cs
   export DISCOVERY_MASTER_HOST=$APOLLO_FQDN
   export NO_SSL_VALIDATION=true
   
   # Deploy Starbuck
   export FRONTEND_URL=$STARBUCK_FQDN
   export IS_MASTER=false
   export AMPHORA_VC_PARTNER_URI=http://$APOLLO_FQDN/amphora
   kubectl config use-context kind-starbuck
   helmfile apply
   
   # Deploy Apollo
   export FRONTEND_URL=$APOLLO_FQDN
   export IS_MASTER=true
   export AMPHORA_VC_PARTNER_URI=http://$STARBUCK_FQDN/amphora
   export CASTOR_SLAVE_URI=http://$STARBUCK_FQDN/castor
   kubectl config use-context kind-apollo
   helmfile apply

Script-based

WARNING: This method of deploying Klyshko is deprecated. The respective scripts will be removed soon.

The deploy.sh scripts in the hack folders (top-level and within modules) can be used to (re-)deploy Klyshko to a 2-party Carbyne Stack VC setup as described in the tutorials on the Carbyne Stack website. To trigger (re-)deployment, the top-level script must be called from the project root folder using

./hack/deploy.sh

Logging

Verbosity

The Klyshko operator uses the logging infrastructure provided by the Operator SDK. To adjust the logging verbosity set the zap-log-level flag to either info, error, or any integer value > 0 (higher values = more verbose, see table below).

apiVersion: apps/v1
kind: Deployment
# ...
spec:
  template:
    spec:
      containers:
        # ...
        - name: manager
          args:
            # ...
            - "--zap-log-level=<<LOG-LEVEL>>"

Choosing Log Levels

We use the following logging level convention in the Klyshko code basis.

Meaning	Level	Command
Essential	0	logger.Info()/Error()
Debug	5	logger.V(DEBUG).Info()/Error()
Tracing	10	logger.V(TRACE).Info()/Error()

License

Carbyne Stack Klyshko Correlated Randomness Generation Service is open-sourced under the Apache License 2.0. See the LICENSE file for details.

3rd Party Licenses

For information on how license obligations for 3rd party OSS dependencies are fulfilled see the README file of the Carbyne Stack repository.

Contributing

Please see the Carbyne Stack Contributor's Guide .