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LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables. (arXiv:2107.08094v2 [cs.CR] UPDATED)
July 1, 2022, 1:20 a.m. | Rachit Rajat, Yongqin Wang, Murali Annavaram
cs.CR updates on arXiv.org arxiv.org
Data confidentiality is becoming a significant concern, especially in the
cloud computing era. Memory access patterns have been demonstrated to leak
critical information such as security keys and a program's spatial and temporal
information. This information leak poses an even more significant privacy
challenge in machine learning models with embedding tables. Embedding tables
are routinely used to learn categorical features from training data. Even
knowing the locations of the embedding table entries accessed, not the data
within the embedding table, …
More from arxiv.org / cs.CR updates on arXiv.org
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