Path ORAM: an extremely simple oblivious RAM protocol

By Emil Stefanov, Marten van Dijk, Elaine Shi, Christopher W. Fletcher, Ling Ren, Xiangyao Yu, Srinivas Devadas. IACR Cryptology ePrint Archive, 2012

1,129 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/63b19c3a46ad4ad054c0e8cfe482c205d29ca3aa

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Citations in the corpus

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors (2018, methodology): “Data-oblivious algorithms [26, 41, 52, 27] are designed to protect memory addresses against the adversary described in §2.”
Sanctum: Minimal Hardware Extensions for Strong Software Isolation (2016, methodology): “Using a straightforward page-coloring-based cache partitioning scheme with Sanctum adds a few percent of overhead in execution time, which is orders of magnitude lower than the overheads of the ORAM schemes [22, 48] that are usually employed to conceal memory access patterns.”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, methodology): “Path ORAM (Stefanov et al., 2013) added a stash to the design as a fixed-size auxiliary storage for overflow elements, which would be scanned on each request. The addition of a small stash enabled a more efficient eviction strategy than the original binary-tree ORAM. Instead of selecting two random ”
Blurry-ORAM: A Multi-Client Oblivious Storage Architecture (2016, methodology): “As far as the security against the server is concerned, we follow the classical access pattern privacy definition [16]:”
HOP: Hardware makes Obfuscation Practical (2017, methodology): “We hide access pattern by using an Oblivious RAM (ORAM), which makes a polylogarithmic number of physical memory accesses to serve each logical memory request from the processor [52].”
The Melbourne Shuffle: Improving Oblivious Storage in the Cloud (2014, background): “For this reason, the “inner-loop” computation required by such simulations is a data-oblivious shuffling operation, which moves a set of items to random locations in fashion that disallows the server to correlate the previous locations of items with their new locations.”
Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound (2015, methodology): “[52] to parametrization the recursion, such that the recursion does not introduce additional asymptotic cost in terms of circuit size.”
SGX-LKL: Securing the Host OS Interface for Trusted Execution (2019, methodology): “Existing techniques from oblivious RAM (ORAM) [29, 39, 59, 89, 90, 102] can be used to provide oblivious access to blocks.”
Multi-client Oblivious RAM Secure Against Malicious Servers (2017, methodology): “[24] show that if B = Ω(log(2)N) bit, communication complexity can be reduced to O(logN) blocks.”
Solidus: Confidential Distributed Ledger Transactions via PVORM (2017, methodology): “The most practical ORAM constructions maintain a small local cache on the client known as a stash and either organize the data blocks as a tree allowing logarithmic work on each access [58, 63], or write to completely randomized locations, resulting in constant-time writes but linear reads (so-calle”
Efficient, Oblivious Data Structures for MPC (2014, methodology): “A.4 indicate that, as in [28], a stash size of O ( k ) is needed for overﬂow probability of 2 − k for a single access.”
Survey of Attacks and Defenses on Edge-Deployed Neural Networks (2019, background): “Since the attacker can still probe peripherals, the device must encrypt data in RAM.”
Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks (2018, background): “However, even an ecient hardware implementation [3] of the state-of-theart ORAM algorithm [15] signicantly increases the number of memory accesses, and likely to result in signicant overhead for”
Oblivious Multi-Party Machine Learning on Trusted Processors (2016, background): “Though recent advances in this space [62] have significantly decreased the ORAM overhead, there are cases where the default solution does not always meet system requirements.”
Data Security and Privacy in Cloud Computing (2014, neutral): “proposed that a path ORAM algorithm is state-of-the-art implementation [45].”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, methodology): “Unfortunately, such a channel could leak information about the data [17, 20, 45].”