This repository is the official implementation of PyAMI for the paper: Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction
The PyAMI project implements the AMI authentication and key generation protocol in a multi-agent system consisting of multiple virtual machines. It also has an option to run the multi-agent system instead entirely on a local machine if no remote machines are available.
If multi-agent system specified to run on remote machines, run.py will use pip to install the latest version of the following packages on provided machines:
The following sections describe how to get up and running with the supported multi-agent system deployments:
- Local directory structure.
- JSON file format for agent parameters.
- Run commands for supported deployments.
The run.py requires a specific directory/subdirectory structure on the local machine for agent information:
agent_info_files/
agent0_info.txt
agent1_info.txt
agent2_info.txt
shared_secrets_files/
agent0_secrets.txt
agent1_secrets.txt
agent2_secrets.txt
agent_neuralnet_models/
agent0_neuralnet_models/
agent1.pth
agent2.pth
agent1_neuralnet_models/
agent1.pth
agent2_neuralnet_models/
agent2.pth
secret_neuralnet_models/
agent0_secret_models/
agent1.pth
agent2.pth
agent1_secret_models/
agent0.pth
agent2_secret_models/
agent0.pth
Note: agent_neuralnet_models/'s subdirectories may contain none or less than group size neural net models, as agents may or may not use a neural net as their behavioral model. The same is true for secret_neuralnet_models/.
Prefacing directory names: The outer directories agent_info_files/, shared_secrets_files/, agent_neuralnet_models/, and secret_neuralnet_models/ may be prefaced with the letter c_ for centralized or d_ indicating decentralized.
Example: c_agent_info_files/, etc.
Below are the required directories for the centralized setting. Repeat the below procedure using the decentralized prefix to run that setting.
c_agent_neuralnet_models/
- Provided in repo.
c_shared_secrets_files/
- Provided in repo.
c_agent_neuralnet_models/
- Create directory and subdirectories by:
mkdir c_agent_neuralnet_models/ mkdir c_agent_neuralnet_models/agent0_neuralnet_models/ mkdir c_agent_neuralnet_models/agent1_neuralnet_models/ mkdir c_agent_neuralnet_models/agent2_neuralnet_models/
- The inner subdirectories are empty by default, but the subdirectories themselves must be created for the program to run.
c_secret_neuralnet_models/
- Create directory and subdirectories by:
mkdir secret_neuralnet_models/ mkdir secret_neuralnet_models/agent0_secret_models/ mkdir secret_neuralnet_models/agent1_secret_models/ mkdir secret_neuralnet_models/agent2_secret_models/
- Again, the inner subdirectories are empty by default, but the subdirectories themselves must be created for the program to run.
There are two multi-agent deployed settings available, along with their own agent classes:
- Centralized setting.
- Central Server Agent (aka 'the central server')
- Centralized System Agent
- Decentralized setting.
- Decentralized System Agent
In this setting, a central authority is entrusted with authentication of and key distribution to all users. This is similar to a public-key infrastructure where a certifying authority provides assurance of a user’s identity.
The default group size is 3 agents. The central server agent (class: Central Server Agent) typically has group ID of 0. The other two agents (class: Centralized System Agent) have group ID 1 and 2, respectively.
Below are important adjustable parameters for these agents per file.
Central Server Agent
agent0_info.txt:
group_id: 0
# The central server typically has group ID of 0.
is_central_server: true
# Identify this agent as the central server.
central_server_group_id: 0
auth_method: [-1, 'hypothesis_test', 'hypothesis_test']
# Authentication test for *agent 1* and *agent 2*. No authentication of self, so leave index 0 as null.
ip_addr: 34.82.122.236
# IP address of remote machine for this agent.
models_list: [-1, {...}, {...}]
# The central server stores behavioral models to interact with agent 1 and agent 2 each in the multi-agent system.
agent0_secrets.txt:
secrets_list: [-1, {...}, {...}]
# The central server stores the true behavioral model belonging to agent 1 and agent 2 as shared secrets.
Centralized System Agent
agent1_info.txt:
group_id: 1
is_central_server: false
# Identify that self is not central seerver.
central_server_group_id: 0
auth_method: 'hypothesis_test'
ip_addr: 35.197.123.164
# IP address of remote machine for this agent.
models_list: [-1, {...}, -1]
# Store a behavioral model at own index, to interact (only) with central server in the multi-agent system.
agent1_secrets.txt:
secrets_list: [{...},-1,-1]
# Store the true model of the central server at index 0 as a shared secret.
agent2_info.txt:
group_id: 2
is_central_server: false
# Identify that self is not central server.
central_server_group_id: 0
auth_method: 'hypothesis_test'
ip_addr: 35.199.162.254
# IP address of remote machine for this agent.
models_list: [-1, -1, {...}]
# Store a behavioral model at own index, to interact (only) with central server in the multi-agent system.
agent2_secrets.txt:
secrets_list: [{...},-1,-1]
# Store the true model of the central server at index 0 as a shared secret.
In this setting, no central authority exists, so users are individually responsible for authentication and key establishment.
The default group size is 3 agents. There is only one class of agent in this system. The decentralized system agents (class: Decentralized System Agent) have group IDs of 0, 1, and 2.
Below are important adjustable parameters for these agents per file.
Decentralized System Agent Decentralized system agents by default use multi-trees, a probabilistic decision tree variant which takes as input actions from all agents in the system. Agents generate multitree parameters from a unique seed parameter, which typically has the value of their group ID.
agent0_info.txt:
group_id: 0
models_list: [{'model_type':'multitree', 'unique_seed': 0,...}, -1, -1]
# Keep own model at own group index. Use multitree for group interaction process. Unique seed for multitree parameter generation is own index.
agent0_secrets.txt:
secrets_list: [-1, {'model_type':'multitree', 'unique_seed': 1,...}, {'model_type':'multitree', 'unique_seed': 2,...}]
# Store true behavioral models of *agent 1* and *agent 2* at their respective indices.
agent1_info.txt:
group_id: 1
models_list: [-1, {'model_type':'multitree', 'unique_seed': 0,...}, -1]
# Keep own model at own group index. Use multitree for group interaction process. Unique seed for multitree parameter generation is own index.
agent1_secrets.txt:
secrets_list: [{'model_type':'multitree', 'unique_seed': 0,...}, -1, {'model_type':'multitree', 'unique_seed': 2,...}]
# Store true behavioral models of *agent 0* and *agent 2* at their respective indices.
agent2_info.txt:
group_id: 2
models_list: [-1, -1, {'model_type':'multitree', 'unique_seed': 2,...}]
# Keep own model at own group index.
agent2_secrets.txt:
secrets_list: [ {'model_type':'multitree', 'unique_seed': 0,...}, {'model_type':'multitree', 'unique_seed': 1,...}, -1]
# Store true behavioral models of *agent 0* and *agent 1* at their respective indices.
PyAMI multi-agent system may be deployed either across remote machines (if available), or entirely on local machine for ease of use.
Agent json info files (agent0_info.txt, agent1_info.txt, agent2_info.txt) must contain IP address of live remote machines agents will run on, in "ipaddr" field as a string.
Example:
"ipaddr": "34.82.122.236"Then run with:
python run.py --system_type centralized --run_remotely yes --group_size 3 --path_agent_files agent_info_files --path_shared_secrets shared_secret_files --path_neuralnet_models agent_neuralnet_models --path_secret_neuralnets secret_neuralnet_models --system_type centralizedProvide IP addresses as in centralized case, then run with:
python run.py --system_type decentralized --run_remotely yes --group_size 3 --path_agent_files agent_info_files --path_shared_secrets shared_secret_files --path_neuralnet_models agent_neuralnet_models --path_secret_neuralnets secret_neuralnet_models Results:
Agent output files: Results for authentication and symmetric session keys are written to individual output files for each agent, and stored on the local machine.
file_out_0.txt
file_err_0.txt
Sample authentication results (centralized setting): A central server successfully authenticates agents a1 and a2:
Agent auth results: [-1, True, True]
Sample session key results (centralized setting): A central server creates group session key for two agents:
group_key: 0.302,0.390,0.328,0.329,0.329*0.355,0.330,0.433,0.318,0.420*0.964,0.406,0.786,0.881,0.900*0.533,0.676,0.844,0.844,0.972
Simply run with:
python run.py --system_type centralized --run_remotely no --group_size 3 --path_agent_files agent_info_files --path_shared_secrets shared_secret_files --path_neuralnet_models agent_neuralnet_models --path_secret_neuralnets secret_neuralnet_models Results: The results are simply printed to the screen, instead of being stored as local output files.
Below, the central server performs successful mutual authentication with agent 1 and agent 2:
Authentication results for all agents:
agent 0 : [-1, True, True]
agent 1 : [True, -1, -1]
agent 2 : [True, -1, -1]
and all agents possess an identical group session key (shown unhashed).
Key agreement results for all agents:
agent 0 : 0.302,0.417,0.275,0.335,0.329,0.264,0.277,0.402,0.385,0.264*0.355,0.330,0.433,0.318,0.420,0.248,0.371,0.248,0.201,0.377*0.964,0.594,0.900,0.079,0.930,0.900,0.186,0.843,0.133,0.881*0.533,0.676,0.844,0.844,0.972,0.985,0.972,0.951,0.726,0.950
agent 1 : 0.302,0.417,0.275,0.335,0.329,0.264,0.277,0.402,0.385,0.264*0.355,0.330,0.433,0.318,0.420,0.248,0.371,0.248,0.201,0.377*0.964,0.594,0.900,0.079,0.930,0.900,0.186,0.843,0.133,0.881*0.533,0.676,0.844,0.844,0.972,0.985,0.972,0.951,0.726,0.950
agent 2 : 0.302,0.417,0.275,0.335,0.329,0.264,0.277,0.402,0.385,0.264*0.355,0.330,0.433,0.318,0.420,0.248,0.371,0.248,0.201,0.377*0.964,0.594,0.900,0.079,0.930,0.900,0.186,0.843,0.133,0.881*0.533,0.676,0.844,0.844,0.972,0.985,0.972,0.951,0.726,0.950
Simply run with:
python run.py --system_type decentralized --run_remotely no --group_size 3 --path_agent_files agent_info_files --path_shared_secrets shared_secret_files --path_neuralnet_models agent_neuralnet_models --path_secret_neuralnets secret_neuralnet_modelsResults:
Below, each agent successfully authenticates all others:
Authentication results for all agents:
agent 0 : [-1, True, True]
agent 1 : [True, -1, True]
agent 2 : [True, True, -1]
and all agents compute an identical group session key (shown unhashed):
Key agreement results for all agents:
agent 0 : 0.937,0.957,0.739,0.656,0.995,0.978,0.995,0.555,0.700,0.345*0.964,0.963,0.987,0.343,0.274,0.427,0.703,0.396,0.998,0.239*0.426,0.404,0.524,0.838,0.992,0.190,0.597,0.148,0.997,0.980
agent 1 : 0.937,0.957,0.739,0.656,0.995,0.978,0.995,0.555,0.700,0.345*0.964,0.963,0.987,0.343,0.274,0.427,0.703,0.396,0.998,0.239*0.426,0.404,0.524,0.838,0.992,0.190,0.597,0.148,0.997,0.980
agent 2 : 0.937,0.957,0.739,0.656,0.995,0.978,0.995,0.555,0.700,0.345*0.964,0.963,0.987,0.343,0.274,0.427,0.703,0.396,0.998,0.239*0.426,0.404,0.524,0.838,0.992,0.190,0.597,0.148,0.997,0.980