760 research outputs found
More is Less: Perfectly Secure Oblivious Algorithms in the Multi-Server Setting
The problem of Oblivious RAM (ORAM) has traditionally been studied in a
single-server setting, but more recently the multi-server setting has also been
considered. Yet it is still unclear whether the multi-server setting has any
inherent advantages, e.g., whether the multi-server setting can be used to
achieve stronger security goals or provably better efficiency than is possible
in the single-server case.
In this work, we construct a perfectly secure 3-server ORAM scheme that
outperforms the best known single-server scheme by a logarithmic factor. In the
process, we also show, for the first time, that there exist specific algorithms
for which multiple servers can overcome known lower bounds in the single-server
setting.Comment: 36 pages, Accepted in Asiacrypt 201
Efficient Data-Oblivious Computation
The rapid increase in the amount of data stored by cloud servers has resulted in growing privacy concerns for users. First, although keeping data encrypted at all times is an attractive approach to privacy, encryption may preclude mining and learning useful patterns from data. Second, companies are unable to distribute proprietary programs to other parties without risking the loss of their private code when those programs are reverse engineered. A challenge underlying both those problems is that how data is accessed — even when that data is encrypted — can leak secret information.
Oblivious RAM is a well studied cryptographic primitive that can be used to solve the underlying challenge of hiding data-access patterns. In this dissertation, we improve Oblivious RAMs and oblivious algorithms asymptotically. We then show how to apply our novel oblivious algorithms to build systems that enable privacy-preserving computation on encrypted data and program obfuscation.
Specifically, the first part of this dissertation shows two efficient Oblivious RAM algorithms: 1) The first algorithm achieves sub-logarithmic bandwidth blowup while only incurring an inexpensive XOR computation for performing Private Information Retrieval operations, and 2) The second algorithm is the first perfectly-secure Oblivious Parallel RAM with bandwidth blowup, depth blowup, and space blowup when the PRAM has CPUs and stores blocks of data. The second part of this dissertation describes two systems — HOP and GraphSC — that address the problem of computing on private data and the distribution of proprietary programs. HOP is a system that achieves simulation-secure obfuscation of RAM programs assuming secure hardware. It is the first prototype implementation of a provably secure virtual black-box (VBB) obfuscation scheme in any model under any assumptions. GraphSC is a system that allows cloud servers to run a class of data-mining and machine-learning algorithms over users’ data without learning anything about that data. GraphSC brings efficient, parallel secure computation to programmers by allowing them to express computation tasks using the GraphLab abstraction. It is backed by the first non-trivial parallel oblivious algorithms that outperform generic Oblivious RAMs
Lower Bounds for Oblivious Near-Neighbor Search
We prove an lower bound on the dynamic
cell-probe complexity of statistically
approximate-near-neighbor search () over the -dimensional
Hamming cube. For the natural setting of , our result
implies an lower bound, which is a quadratic
improvement over the highest (non-oblivious) cell-probe lower bound for
. This is the first super-logarithmic
lower bound for against general (non black-box) data structures.
We also show that any oblivious data structure for
decomposable search problems (like ) can be obliviously dynamized
with overhead in update and query time, strengthening a classic
result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page
What Storage Access Privacy is Achievable with Small Overhead?
Oblivious RAM (ORAM) and private information retrieval (PIR) are classic
cryptographic primitives used to hide the access pattern to data whose storage
has been outsourced to an untrusted server. Unfortunately, both primitives
require considerable overhead compared to plaintext access. For large-scale
storage infrastructure with highly frequent access requests, the degradation in
response time and the exorbitant increase in resource costs incurred by either
ORAM or PIR prevent their usage. In an ideal scenario, a privacy-preserving
storage protocols with small overhead would be implemented for these heavily
trafficked storage systems to avoid negatively impacting either performance
and/or costs. In this work, we study the problem of the best $\mathit{storage\
access\ privacy}\mathit{small\ overhead}\mathit{differential\ privacy\ access}\mathit{oblivious\ access}\epsilon = \Omega(\log n)\epsilon = \Theta(\log n)O(1)\epsilon = \Theta(\log n)O(\log\log n)$
overhead. This construction uses a new oblivious, two-choice hashing scheme
that may be of independent interest.Comment: To appear at PODS'1
Data-Oblivious Graph Algorithms in Outsourced External Memory
Motivated by privacy preservation for outsourced data, data-oblivious
external memory is a computational framework where a client performs
computations on data stored at a semi-trusted server in a way that does not
reveal her data to the server. This approach facilitates collaboration and
reliability over traditional frameworks, and it provides privacy protection,
even though the server has full access to the data and he can monitor how it is
accessed by the client. The challenge is that even if data is encrypted, the
server can learn information based on the client data access pattern; hence,
access patterns must also be obfuscated. We investigate privacy-preserving
algorithms for outsourced external memory that are based on the use of
data-oblivious algorithms, that is, algorithms where each possible sequence of
data accesses is independent of the data values. We give new efficient
data-oblivious algorithms in the outsourced external memory model for a number
of fundamental graph problems. Our results include new data-oblivious
external-memory methods for constructing minimum spanning trees, performing
various traversals on rooted trees, answering least common ancestor queries on
trees, computing biconnected components, and forming open ear decompositions.
None of our algorithms make use of constant-time random oracles.Comment: 20 page
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