Breaking the Oblivious-RAM Bandwidth Wall

2018 ◽  
Author(s):  
Hamza Omar ◽  
Syed Kamran Haider ◽  
Ling Ren ◽  
Marten van Dijk ◽  
Omer Khan
Keyword(s):  
Oblivious Ram

Is There an Oblivious RAM Lower Bound for Online Reads?

2021 ◽  
Vol 34 (3) ◽  
Author(s):  
Mor Weiss ◽  
Daniel Wichs
Keyword(s):  
Lower Bound ◽  
Oblivious Ram

LolliRAM: A Cross-Layer Design to Exploit Data Locality in Oblivious RAM

2021 ◽  
Author(s):  
Yi Wang ◽  
Weixuan Chen ◽  
Xianhua Wang ◽  
Rui Mao
Keyword(s):  
Data Locality ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Oblivious Ram

OptORAMa: Optimal Oblivious RAM

2020 ◽  
pp. 403-432
Author(s):  
Gilad Asharov ◽  
Ilan Komargodski ◽  
Wei-Kai Lin ◽  
Kartik Nayak ◽  
Enoch Peserico ◽  
...  
Keyword(s):  
Oblivious Ram

Locality-Preserving Oblivious RAM

2019 ◽  
pp. 214-243
Author(s):  
Gilad Asharov ◽  
T.-H. Hubert Chan ◽  
Kartik Nayak ◽  
Rafael Pass ◽  
Ling Ren ◽  
...  
Keyword(s):  
Oblivious Ram ◽  
Locality Preserving

scholarly journals FedORAM: A Federated Oblivious RAM Scheme

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 187687-187699
Author(s):  
Alexandre Pujol ◽  
Liam Murphy ◽  
Christina Thorpe
Keyword(s):  
Oblivious Ram

scholarly journals Identifying Influential Spreaders in a Social Network (While Preserving Privacy)

2020 ◽  
Vol 2020 (2) ◽  
pp. 537-557 ◽  
Author(s):  
Varsha Bhat Kukkala ◽  
S.R.S Iyengar
Keyword(s):  
Social Network ◽  
Data Structures ◽  
Private Information ◽  
Traditional Approach ◽  
Ranking Methods ◽  
Graph Data ◽  
Oblivious Ram ◽  
Underlying Graph ◽  
Efficient Data ◽  
Pagerank Centrality

AbstractIn order to disseminate information in a social network, it is important to first identify the influential spreaders in the network. Using them as the seed spreaders, the aim is to ensure that the information is cascaded throughout the network. The traditional approach to identifying influential nodes is to determine the top-r ranked nodes in accordance with various ranking methods such as PageRank, k-Shell decomposition, ClusterRank and VoteRank. In the current work, we study the problem of ranking the nodes when the underlying graph is distributedly held by a set of individuals, who consider their share of the data as private information. In particular, we design efficient secure multiparty computation (MPC) protocols for k-Shell decomposition, PageRank and VoteRank. For improved efficiency, we employ the oblivious RAM construct in conjunction with efficient data-oblivious graph data structures. We are the first to propose a secure variant of the VoteRank algorithm. We prove that the proposed protocols are asymptotically more efficient and have lower runtime in practice than the previous best known MPC protocols for computing k-Shell decomposition and PageRank centrality scores.

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