Contact The MuchPIR Team: muchpir@pm.me
Author: Escanor Liones (Single Author)
What is PIR?
Private Information Retrieval refers to the ability to query a database without disclosing which item is looked up or whether that item exists at all on the database. Not only is the query kept confidential, but so is the result of the query. In particular, any observer, including the platform running the query and hosting the databse itself, cannot tell whether the data returned contains the result to our query or not, or which item was being looked up.
Who will care?
- Law Enforcement: you want to look up data on bad guy A without disclosing to the company holding the data, or any third party holding or processing the query, that you are looking for data about A.
- Stock Exchange: aggregate data on symbol look up can disclose market interest in advance of price movement. A PIR based symbol look up would not disclose to any third parties, or the Stock Exchange itself, handling the symbol price, volume, short/long interest, etc queries any market interest in advance of price movement.
Who else will care?
Applications that require long term confidentiality guarantees may be interested in using PIR. These types of applications might need additional layers of security for a robust, secure implementation with confidentiality guarantees over long periods of time. The current implementation, even the optimized version, works when the database data itself is not encrypted. If the database data itself were to be encrypted, in addition to the queries, then the entities that will care are:
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Banks: customer data can be encrypted, stored in cloud services, and queried as needed for the banks daily operations. As the data itself is encrypted it matters not whether the cloud service is hacked, not trusted, or that the hardware it is running on has bugs (meltdown/specter, etc). And because the data can be operated on under encryption, the bank may do its business as usual, or at least offload a significant part of it to the cloud with security guarantees.
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Hospitals: Same as banks. Offload data to the cloud encrypted and perform computations under encryption. Only the entity holding the private keys, that is the hospitals/banks, can decrypt that data and queried results on that data.
MuchPIR is Private Information Retrieval using Homomorphic Encryption implemented as a C/C++ Aggregate extension to Postgres. This demo is good a enough implementation for folks who want to experiment with PIR on Postgres and have a working C/C++ extension.
The latest version of the software this demo is based on has the following characteristics:
Supported table size: > 10 Million Rows.
Query Compute Performance: under a minute.
Network Query Size: From about 1 MB to less than 10 MB. (Depending on configuration.)
Query Result Size: < 512 KB.
Hardware Used: x86 architecture. (Tested on AMD 2nd Gen Epyc Processor)
Security: >= 128 bits of quantum and classical security.
Integration: Just a plug in to postgres.
Encrypted data: Database/tables/schemas need no changes at all.
Cryptographic Keys: Every single query has a new private/public key pair.
Highly optimized parallelized PIR query.
The MuchPIR Team would like to point out that these parameters and performance listed are meant to encompass a wide range of use cases. If your demand for this level of privacy is for a significantly smaller databases, or requires less data bandwidth, then performance goes up and bandwidth goes down on query/response sizes. Similarly, if your databases are much larger or your bandwidth needs much bigger.
We may also speak of implementations where bandwidth and performance tradeoffs are parametrizable by the user making the query as part of the query itself. These would be implemented on a case by case basis to fit the need of your organization.
This demo targets string types on postgres, but can easily be adapted to query any type: int4, int8, float, bytea, etc.
There are three other uses of this technology beyond PIR that we have developed concepts for:
- Limited String Search: small words, such as a name or last name, could be searched for instead of indexing into a particular row.
- Exact Match Search: within limits this could be done as well. Preferably, the search space would smaller than database size.
- Unique ID Search: where data could be searched based on unique ID. This would works well on columns with unique data.
- Accumulation: Aggregating data by addition on a given column in a way that is indistinguishable from PIR is also possible. (Same performance, same everything.)
The team is still developing other use cases. Feel free to contact us if there is a use case of interest to you, your team or organization.
This code has been ran and tested on Ubuntu 18.04 and Ubuntu 20.04. It does require:
- Microsoft SEAL Library Version 3.5.8: https://github.com/microsoft/SEAL/tree/v3.5.8
- Postgres C++ wrapper libpqxx: https://github.com/jtv/libpqxx
- Postgres Development Packages for C/C++ extensions.
Let us take MuchPIR for a test run.
git clone https://github.com/ReverseControl/MuchPIR.git
cd MuchPIR
docker build -t muchpir:1.0 .
docker run -e POSTGRES_PASSWORD=pass -e POSTGRES_USER=postgres muchpir:1.0
This will start the container that runs the postgres server with our extension. Now we want to enter the docker container while it is running. In another terminal, we do:
docker container ls
docker exec -u 0 -ti <container-name> bash
By default you will enter in the right directory to run:
su - postgres
cd /data/
psql -U postgres -d testdb -f ./load_data.sql
exit
This will load data into tables that we will use for testing. The data comes from the IMDB dataset you find here: https://datasets.imdbws.com/name.basics.tsv.gz. We took the first 69,420 lines, excluding the first line which is the header for the columns, and created several tables.
TABLE Size | Signature for the tables
data_10 1024 | table_N (
data_11 2048 | nconst bigint,
data_12 4096 | primaryName text,
data_13 8192 | birthYear bigint,
data_14 16384 | deathYear bigint,
data_15 32768 | primaryProfession text,
data_16 65536 | knownForTitles text
data_all 69420 | );
Postgres extensions are installed per database. Now that we have installed our extension on the system (docker container) and Postgres knows where to find it, we can now install it on our database so we may use it.
su - postgres
cd /data/
psql -U postgres -d testdb -f /git/MuchPIR/pir_select--1.0.sql
exit
Now that we have the database running with the extension installed on a databse with tables and data ready to go, let's build the client for testing.
cd /git/MuchPIR/client
g++ -std=c++17 -march=native -I/usr/local/include/SEAL-3.5/ -L/usr/local/lib/ client_module.cpp -lpqxx -lpq -l:libseal.so.3.5
time ./a.out
You should get an output like this:
root@24a82b6e3437:/git/MuchPIR/client# g++ -std=c++17 -march=native -I/usr/local/include/SEAL-3.5/ -L/usr/local/lib/ client_module.cpp -lpqxx -lpq -l:libseal.so.3.5
root@24a82b6e3437:/git/MuchPIR/client# time ./a.out
Note: compression is disabled. This is the maximum size for each object at this database size under current parameters.
Plain Modulus: 40961
db_size: 1024 <-- Note: By default 1024 in this demo. Can be increased up to 10s of millions: requires math and code, i.e the latest version.
row_index: 0 <-- Note: You can change this to any row you want in code.
hcube_dim: 1 <-- Note: Hypercube dimension. This demo supports only 1 dimension. Limits DB_size to 4096 rows.
hecube_len: 1024 <-- Note: An optimization. Does not affect this demo. Improves performance for high dimensional hyper cubes.
Polynomial Degree(N): 4096
Parameters size: 129 bytes.
Galois Keys size: 1659085 bytes.
Hypercube embedding size: 131177 bytes.
Query Size: 1790391 bytes.
Result Size: 182362 bytes.
Query result (Polynomial): 46x^11 + 72x^10 + 65x^9 + 64x^8 + 20x^7 + 41x^6 + 73x^5 + 74x^4 + 61x^3 + 69x^2 + 72x^1 + 65
Query result (ASCII) : Fred Astaire
real 0m53.250s
user 0m0.216s
sys 0m0.017s
