Benjamin M. Case's research while affiliated with Clemson University and other places
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Publications (4)
![Utilization ratio of proposed design (L = [1, 16]) with N = 2ⁿ, n ∈ [5,...](publication/345808402/figure/fig4/AS:962206267097088@1606419259080/Utilization-ratio-of-proposed-design-L-1-16-with-N-2-n-5-12_Q320.jpg)
![Comparison of latency with prior works: Chen [5], Pöppelmann [25].](publication/345808402/figure/fig5/AS:962206267101185@1606419259324/Comparison-of-latency-with-prior-works-Chen-5-Poeppelmann-25_Q320.jpg)
Fully homomorphic encryption (FHE) is a post-quantum secure cryptographic technology that enables privacy-preserving computing on an untrusted platform without divulging any secret or sensitive information. The core of FHE is the bootstrapping algorithm, which is the intermediate refreshing procedure of a processed ciphertext. However, this step ha...
We propose a smartphone app named HappyKidz that allows parents to monitor their child’s well-being in a non-invasive way based on measurable behavioral indicators. The app collects behavioral data on smartphone usage, encrypts them with homomorphic encryption, and sends the encrypted data to a server. The server calculates a well-being score for t...
Thanks to the inherent post-quantum resistant properties, lattice-based cryptography has gained increasing attention in various cryptographic applications recently. To facilitate the practical deployment, efficient hardware architectures are demanded to accelerate the operations and reduce the computational resources, especially for the polynomial...
Error distribution plays a central role in the security of encryption based on the Learning with Errors (LWE) problem and its variants. In this paper, we investigate the error distribution of weak Poly-LWE instances. For this purpose, we derive a closed-form formula to compute the mapped error distribution. With this algebraic approach to evaluate...
Citations
... A particularly interesting application area is to enable machine learning to be done on data while it is secured by encryption. Some work in this direction of implementing machine learning and deep neural nets using homomorphic encryption has been done as in [1,2,3,5]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/982501241679877-1611257958882_Q64/Monika-Trimoska.jpg)
... In particular, the efficient designs for modular polynomial multiplier have been extensively studied, using number theoretic transform (NTT) [10][11][12], schoolbook polynomial multiplication algorithm [13,14], or Karatsuba algorithm [9,15]. The architectures for modular multiplier and hash module have also been investigated in [16][17][18][19]. In contrast, hardware architectures for the sampler are less studied. ...
... If it is constant, it shows that the specified work supports a parameter in a constant range, i.e., constant log 2 (q) bit-size. To the best of our knowledge, there are only two designs in the table, which propose NTT architectures providing compile-time configurability in terms of area and performance [20], [21]. While the architecture in [20] supports only the NTT operation, the one in [21] supports large scheme parameters typically used in homomorphic encryption applications. ...
... An explicit method of calculating the probability distribution of ( ) e α given the distribution of the polynomial coefficients of e is presented in ref. [4]. ...
