Homomorphic encryption (HE) enables privacy-preserving machine learning by allowing computation directly over encrypted data, and HE-based inference algorithms are already practical even for relatively large Convolutional Neural Networks (CNNs). …
Transient execution attacks on modern processors continue to threaten security by stealing sensitive data from other processes running on the same CPU. A recent example is Downfall, which demonstrated how microarchitecture leakage could reveal short …
Over two decades since their introduction in 2005, all major verifiable pairing delegation protocols for public inputs have been designed to ensure unconditional security. However, we note that a delegation protocol involving only ephemeral secret …
Several prior works have suggested to use non-interactive arguments of knowledge with short proofs to aggregate signatures of Falcon, which is part of the first post-quantum signatures selected for standardization by NIST. Especially LaBRADOR, based …
Homomorphic encryption (HE) enables computation on encrypted data, which in turn facilitates the outsourcing of computation on private data. However, HE offers no guarantee that the returned result was honestly computed by the cloud. In order to have …
We generalize the Bernstein-Yang (BY) algorithm for constant-time modular inversion to compute the Kronecker symbol, of which the Jacobi and Legendre symbols are special cases. We first develop a basic and easy-to-implement algorithm, defined with …
The security of modern cryptography depends on multiple factors, from sound hardness assumptions to correct implementations that resist side-channel cryptanalysis. Curve-based cryptography is not different in this regard, and substantial progress in …
The edit distance is a metric widely used in genomics to measure the similarity of two DNA chains. Motivated by privacy concerns, we propose a 2PC protocol to compute the edit distance while preserving the privacy of the inputs. Since the edit …