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A GPU implementation of fully homomorphic encryption on torus

This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.

Usage example

importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)

Performance

PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-

About

NuCypher fully homomorphic encryption (NuFHE) library implemented in Python

nufhe.readthedocs.io/en/latest/

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GPL-3.0 license
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var __re = new RegExp('^' + "github\\.com" + '

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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
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GPL-3.0 license
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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
453 stars
Watchers
2 watching
Forks
52 forks
Report repository
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, 'i');
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observer.observe(document.body, { childList: true, subtree: true });
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}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
Resources
Readme
GPL-3.0 license
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453 stars
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2 watching
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52 forks
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, 'i');
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// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
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}
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document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
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});
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});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
453 stars
Watchers
2 watching
Forks
52 forks
Report repository
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Used by
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, 'i');
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observer.observe(document.body, { childList: true, subtree: true });
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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
453 stars
Watchers
2 watching
Forks
52 forks
Report repository
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Used by
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, 'i');
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// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
Resources
Readme
GPL-3.0 license
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2 watching
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52 forks
Report repository
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
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[data-theme="light"], [data-theme="light"],
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pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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A GPU implementation of fully homomorphic encryption on torus
This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.
Usage example
importrandomimportnufhesize=32bits1= [random.choice([False, True]) foriinrange(size)]
bits2= [random.choice([False, True]) foriinrange(size)]
reference= [not (b1andb2) forb1, b2inzip(bits1, bits2)]
ctx=nufhe.Context()
secret_key, cloud_key=ctx.make_key_pair()
ciphertext1=ctx.encrypt(secret_key, bits1)
ciphertext2=ctx.encrypt(secret_key, bits2)
vm=ctx.make_virtual_machine(cloud_key)
result=vm.gate_nand(ciphertext1, ciphertext2)
result_bits=ctx.decrypt(secret_key, result)
assertall(result_bits==reference)
Performance
PlatformLibraryPerformance (ms/bit)
Binary GateMUX Gate
Single Core/Single GPU - FFTTFHE (CPU)1326
nuFHE0.130.22
Speedup100.9117.7
Single Core/Single GPU - NTTcuFHE0.35N/A
nuFHE0.350.67
Speedup1.0-
About
NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
nufhe.readthedocs.io/en/latest/
Resources
Readme
GPL-3.0 license
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2 watching
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52 forks
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