Table 3.
Comparison of various PPML protocols
PPML | Capability |
Threat Model |
Techniques | Neural | |||
---|---|---|---|---|---|---|---|
Inference | Training | Semi-honest | Malicious | Networks | |||
2PC | SecureML [24] | √ | √ | √ | HE/GC/ASS | From [24] | |
2PC | MiniONN [277] | √ | √ | HE/GC/ASS | From [24, 277] | ||
2PC | GAZELLE [20] | √ | √ | HE/GC/ASS | From [24, 277] | ||
2PC | EzPC [278] | √ | √ | GC/ASS | From [24, 277] | ||
2PC | XONN [29] | √ | √ | GC/ASS | VGG-16 [279] | ||
2PC | QUOTIENT [18] | √ | √ | √ | OT/GC/ASS | From [18] | |
2PC | MP2ML [280] | √ | √ | HE/GC/ASS | CryptoNets [281] | ||
2PC | CrypTFlow2 [28] | √ | √ | HE/OT/ASS | DenseNet-121 [282] | ||
2PC | Delphi [22] | √ | √ | HE/GC/ASS | VGG-16 [279] | ||
2PC | QuantizedNN [283] | √ | √ | Abort | HE/OT/ASS | MobileNets [284] | |
2PC | SIRNN [27] | √ | √ | OT/ASS | Heads [285] | ||
3PC | Chameleon [286] | √ | √ | GC/ASS | AlexNet [287] | ||
3PC | ABY3 [23] | √ | √ | √ | GC/ASS | From [24, 277] | |
3PC | ASTRA [288] | √ | √ | √ | Abort | ASS/RSS | From [24] |
3PC | SecureNN [289] | √ | √ | √ | ASS | From [24, 277] | |
3PC | BLAZE [26] | √ | √ | √ | Fairness | ASS/RSS | From [24] |
3PC | QuantizedNN [283] | √ | √ | Abort | RSS | MobileNets [284] | |
3PC | CrypTFlow [21] | √ | √ | ASS | DenseNet-121 [282] | ||
3PC | SWIFT [290] | √ | √ | √ | GOD | ASS/RSS | VGG-16 [279] |
3PC | CryptGPU [31] | √ | √ | √ | RSS | ResNet-152 [291] | |
3PC | Falcon [292] | √ | √ | √ | Abort | RSS | VGG-16 [279] |
4PC | FLASH [293] | √ | √ | √ | GOD | ASS/RSS | From [24] |
4PC | SWIFT [290] | √ | √ | √ | GOD | ASS/RSS | VGG-16 [279] |
4PC | Trident [19] | √ | √ | √ | Fairness | GC/ASS/RSS | From [24] |
4PC | Tetrad [294] | √ | √ | √ | GOD | GC/ASS/RSS | VGG-16 [279] |
Note: All protocols for secure three-party/four-party computation (i.e., 3PC/4PC) tolerate one corruption, and thus belong to the honest-majority setting. For malicious adversaries, “Abort”, “Fairness”, and “GOD” denote the PPML protocols that achieve security with abort, fairness, and guaranteed output delivery, respectively. For the underlying LSSS, we use “ASS” and “RSS” to denote the additive secret sharing and the replicated secret sharing, respectively. If a PPML protocol supports multiple neural-network architectures, we only describe the one with largest parameters for private ML inference.
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