Python wrapper for labeled and unlabeled asymmetric private set intersection (APSI).
Built against APSI 1.1.0.
PyAPSI 0.3 moves from APSI 0.12 to APSI 1.1, which brings a few user visible changes:
- The log levels are named
"TRACE","DEBUG","INFO","WARNING","ERROR"and"SUPPRESS". The previous"ALL"and"OFF"are still accepted as"TRACE"and"SUPPRESS"respectively. - APSI retuned its shipped parameter sets, because most of them were above the documented
2^-40 false-positive probability per query. A server and a client have to agree on their
parameters, so both sides of a deployment should move to the new values together; the
example below is APSI's
parameters/1M-256.json. - APSI's serialization version is unchanged and the schemas stayed wire compatible, so a database saved by an earlier PyAPSI release should still load, but that is untested here. A cuckoo filter written before APSI 1.0 carries no assertion that nothing was dropped from it, so it is loaded as one that may have dropped items; that costs some query time on the affected bin bundles, not correctness.
- Each query a client sends now carries key material of its own, so that a server can no longer tell from the public keys that two queries came from the same client.
pip install apsiThis downloads the source distribution and builds APSI + all dependencies locally. The build automatically selects the best CPU optimizations for your machine.
Requirements:
- C++ compiler with full C++17 support (GCC, Clang, or Visual Studio 2022/2026, as required by APSI 1.1)
- CMake >= 3.25
- Python 3.11–3.14
- Internet access (vcpkg downloads dependencies during build)
Build time: Approximately 5-15 minutes depending on your machine.
On Linux x86_64, the FourQlib elliptic curve assembly optimizations are disabled
because the hand-optimized AVX2 assembly files use non-PIC relocations that are
incompatible with Python shared extensions (.so files). The generic C fallback is
used instead, which results in approximately 2-3x slower OPRF (Oblivious Pseudorandom
Function) operations.
This does not affect the SEAL homomorphic encryption operations, which dominate runtime for typical set intersection sizes. On macOS and Windows, FourQlib uses different optimization paths that are not affected.
For maximum performance on Linux, consider building APSI as a standalone static library outside of Python and using the native CLI tools.
Example usage of the labeled APSI server and client. The unlabeled variant can be used analogous to this.
from apsi import LabeledServer, LabeledClient
apsi_params = """
{
"table_params": {
"hash_func_count": 3,
"table_size": 585,
"max_items_per_bin": 180
},
"item_params": {"felts_per_item": 7},
"query_params": {
"ps_low_degree": 0,
"query_powers": [
1, 3, 4, 6, 10, 13, 15, 21, 29, 37, 45,
53, 61, 69, 77, 81, 83, 86, 87, 90, 92, 96
]
},
"seal_params": {
"plain_modulus": 65537,
"poly_modulus_degree": 4096,
"coeff_modulus_bits": [40, 40, 24]
}
}
"""
server = LabeledServer()
server.init_db(apsi_params, max_label_length=10)
server.add_items([("item", "1234567890"), ("abc", "123"), ("other", "my label")])
client = LabeledClient(apsi_params)
oprf_request = client.oprf_request(["item", "abc"])
oprf_response = server.handle_oprf_request(oprf_request)
query = client.build_query(oprf_response)
response = server.handle_query(query)
result = client.extract_result(response)
assert result == {"item": "1234567890", "abc": "123"}To control multi threading and logging in APSI see
apsi.utils.
pip install -e .
pytest tests/pip install build
python -m build --sdist