Concrete
2.5
K

# Bit Extraction

Some applications require directly manipulating bits of integers. Concrete provides a bit extraction operation for such applications.
Bit extraction is capable of extracting a slice of bits from an integer. Index 0 corresponds to the lowest significant bit. The cost of this operation is proportional to the highest significant bit index.
Bit extraction only works in the `Native` encoding, which is usually selected when all table lookups in the circuit are less than or equal to 8 bits.
from concrete import fhe
@fhe.compiler({"x": "encrypted"})
def f(x):
return fhe.bits(x)[0], fhe.bits(x)[3]
inputset = range(32)
circuit = f.compile(inputset)
assert circuit.encrypt_run_decrypt(0b_00000) == (0, 0)
assert circuit.encrypt_run_decrypt(0b_00001) == (1, 0)
assert circuit.encrypt_run_decrypt(0b_01100) == (0, 1)
assert circuit.encrypt_run_decrypt(0b_01101) == (1, 1)
Slices can be used for indexing `fhe.bits(value)` as well.
from concrete import fhe
@fhe.compiler({"x": "encrypted"})
def f(x):
return fhe.bits(x)[1:4]
inputset = range(32)
circuit = f.compile(inputset)
assert circuit.encrypt_run_decrypt(0b_01101) == 0b_110
assert circuit.encrypt_run_decrypt(0b_01011) == 0b_101
Even slices with negative steps are supported!
from concrete import fhe
@fhe.compiler({"x": "encrypted"})
def f(x):
return fhe.bits(x)[3:0:-1]
inputset = range(32)
circuit = f.compile(inputset)
assert circuit.encrypt_run_decrypt(0b_01101) == 0b_011
assert circuit.encrypt_run_decrypt(0b_01011) == 0b_101
Signed integers are supported as well.
from concrete import fhe
@fhe.compiler({"x": "encrypted"})
def f(x):
return fhe.bits(x)[1:3]
inputset = range(-16, 16)
circuit = f.compile(inputset)
assert circuit.encrypt_run_decrypt(-14) == 0b_01 # -14 == 0b_10010 (in two's complement)
assert circuit.encrypt_run_decrypt(-12) == 0b_10 # -12 == 0b_10100 (in two's complement)
Lastly, here is a practical use case of bit extraction.
import numpy as np
from concrete import fhe
@fhe.compiler({"x": "encrypted"})
def is_even(x):
return 1 - fhe.bits(x)[0]
inputset = [
np.random.randint(-16, 16, size=(5,))
for _ in range(100)
]
circuit = is_even.compile(inputset)
sample = np.random.randint(-16, 16, size=(5,))
for value, value_is_even in zip(sample, circuit.encrypt_run_decrypt(sample)):
print(f"{value} is {'even' if value_is_even else 'odd'}")
prints
13 is odd
0 is even
-15 is odd
2 is even
-6 is even

## Limitations

• Bits cannot be extracted using a negative index.
• Which means `fhe.bits(x)[-1]` or `fhe.bits(x)[-4:-1]` is not supported for example.
• The reason for this is that we don't know in advance (i.e., before inputset evaluation) how many bits `x` has.
• For example, let's say you have `x == 10 == 0b_000...0001010`, and you want to do `fhe.bits(x)[-1]`. If the value is 4-bits (i.e., `0b_1010`), the result needs to be `1`, but if it's 6-bits (i.e., `0b_001010`), the result needs to be `0`. Since we don't know the bit-width of `x` before inputset evaluation, we cannot calculate `fhe.bits(x)[-1]`.
• When extracting bits using slices in reverse order (i.e., step < 0), the start bit needs to be provided explicitly.
• Which means `fhe.bits(x)[::-1]` or `fhe.bits(x)[:2:-1]` is not supported for example.
• The reason is the same as above.
• When extracting bits of signed values using slices, the stop bit needs to be provided explicitly.
• Which means `fhe.bits(x)[1:]` or `fhe.bits(x)[1::2]` is not supported for example.
• The reason is similar to above.
• To explain a bit more, signed integers use two's complement representation. In this representation, negative values have their most significant bits set to 1 (e.g., `-1 == 0b_11111`, `-2 == 0b_11110`, `-3 == 0b_11101`). Extracting bits always returns a positive value (e.g., `fhe.bits(-1)[1:3] == 0b_11 == 3`) This means if you were to do `fhe.bits(x)[1:]` where `x == -1`, if `x` is 4 bits, the result would be `0b_111 == 7`, but if `x` is 5 bits the result would be `0b_1111 == 15`. Since we don't know the bit-width of `x` before inputset evaluation, we cannot calculate `fhe.bits(x)[1:]`.
• Bits of floats cannot be extracted.
• Floats are partially supported but extracting their bits is not supported at all.

## Performance Considerations

### A Chain of Individual Bit Extractions

Key Concept: Extracting a specific bit requires clearing all the preceding lower bits. This involves extracting these previous bits as intermediate values and then subtracting them from the input.
Implications:
• Bits are extracted sequentially, starting from the least significant bit to the more significant ones. The cost is proportional to the index of the highest extracted bit plus one.
• No parallelization is possible. The computation time is proportional to the cost, independent of the number of CPUs.
Examples:
• Extracting `fhe.bits(x)[4]` is approximately five times costlier than extracting `fhe.bits(x)[0]`.
• Extracting `fhe.bits(x)[4]` takes around five times more wall clock time than `fhe.bits(x)[0]`.
• The cost of extracting `fhe.bits(x)[0:5]` is almost the same as that of `fhe.bits(x)[5]`.

### Reuse of Intermediate Extracted Bits

Key Concept: Common sub-expression elimination is applied to intermediate extracted bits.
Implications:
• The overall cost for a series of `fhe.bits(x)[m:n]` calls on the same input `x` is almost equivalent to the cost of the single most computationally expensive extraction in the series, i.e. `fhe.bits(x)[n]`.
• The order of extraction in that series does not affect the overall cost.
Example:
The combined operation `fhe.bit(x)[3] + fhe.bit(x)[2] + fhe.bit(x)[1]` has almost the same cost as `fhe.bits(x)[3]`.

### TLUs of 1b input precision

Each extracted bit incurs a cost of approximately one TLU of 1-bit input precision. Therefore, `fhe.bits(x)[0]` is generally faster than any other TLU operation.