180 citations · 186 across the 4 of their papers we have counts for
4 papers
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
Juntaek Lim, Youngeun Kwon, Ranggi Hwang +3
Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection. While private training of computer vision or natural language pro…
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations
Yujeong Choi, John Kim, Minsoo Rhu
While providing low latency is a fundamental requirement in deploying recommendation services, achieving high resource utility is also crucial in cost-effectively maintaining the d…
DiVa: An Accelerator for Differentially Private Machine Learning
Beomsik Park, Ranggi Hwang, Dongho Yoon +2
The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential p…
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim +4
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption sch…