Data-oblivious graph algorithms for secure computation and outsourcing

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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Popular applications used for the empirical evaluation of practical MPC systems include graph algorithms [5, 32, 42, 51, 57], data structure algorithms [42, 51], string matching and distance algorithms [36, 37, 66] and AES [3, 36, 37, 66].”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, background): “Other interesting data-oblivious algorithms have also been proposed for graph drawing [27] and graph-related computations such as maximum flow, minimum spanning tree, single-source single-destination (SSSD) shortest path, or breadth-first search [12].”
Secure Linear Regression on Vertically Partitioned Datasets (2016, methodology): “Popular applications used for the empirical evaluation of implemented systems include graph algorithms [6, 18, 24, 29, 34, 45], data structure algorithms [24,29,42], string matching and distance algorithms [20,21,39] and AES [4,20,21,39].”
Efficient, Oblivious Data Structures for MPC (2014, methodology): “[3] presented an oblivious algorithm for finding shortest paths in a graph with complexity in O(|V |(2)).”