Supported operations

Here are the operations you can use inside the function you are compiling.
Some of these operations are not supported between two encrypted values. A detailed error will be raised if you try to do something that is not supported.

Supported Python operators

Supported NumPy functions

Supported ndarray methods

Supported ndarray properties


Operational constraints

Some Python control flow statements are not supported. For example, you cannot have an if statement or a while statement for which the condition depends on an encrypted value. However, such statements are supported with constant values (e.g., for i in range(SOME_CONSTANT), if os.environ.get("SOME_FEATURE") == "ON":).
Another constraint is that you cannot have floating-point inputs or floating-point outputs. You can have floating-point intermediate values as long as they can be converted to an integer Table Lookup (e.g., (60 * np.sin(x)).astype(np.int64)).

Bit width constraints

There is a limit on the bit width of encrypted values. We are constantly working on increasing this bit width. If you go above this limit, you will get an error.

Computation constraints

One of the most common operations in Concrete Numpy is Table Lookups (TLUs). TLUs are performed with an FHE operation called Programmable Bootstrapping (PBS). PBSes have a certain probability of error, which, when triggered, result in inaccurate results.
Let's say you have the table:
[1, 4, 9, 16, 25, 36, 49, 64]
And you performed a table lookup using 4, The result you should get is 16, but because of the possibility of error, you can sometimes get 9 or 25.
The probability of this error can be configured through the p_error configuration option, which has the default value of 0.000063342483999973 (i.e., probability of success is 99.993%). Keep in mind that changing it could affect compilation and key generation times.