1,316 research outputs found
Locality-Preserving Oblivious RAM
Oblivious RAMs, introduced by Goldreich and Ostrovsky [JACM\u2796], compile any RAM program into one that is ``memory oblivious\u27\u27, i.e., the access pattern to the memory is independent of the input. All previous ORAM schemes, however, completely break the locality of data accesses (for instance, by shuffling the data to pseudorandom positions in memory).
In this work, we initiate the study of locality-preserving ORAMs --- ORAMs that preserve locality of the accessed memory regions, while leaking only the lengths of contiguous memory regions accessed. Our main results demonstrate the existence of a locality-preserving ORAM with poly-logarithmic overhead both in terms of bandwidth and locality. We also study the tradeoff between locality, bandwidth and leakage, and show that any scheme that preserves locality and does not leak the lengths of the contiguous memory regions accessed, suffers from prohibitive bandwidth.
To the best of our knowledge, before our work, the only works combining locality and obliviousness were for symmetric searchable encryption [e.g., Cash and Tessaro (EUROCRYPT\u2714), Asharov et al. (STOC\u2716)].
Symmetric search encryption ensures obliviousness if each keyword is searched only once, whereas ORAM provides obliviousness to any input program. Thus, our work generalizes that line of work to the much more challenging task of preserving locality in ORAMs
SqORAM: Read-Optimized Sequential Write-Only Oblivious RAM
Oblivious RAM protocols (ORAMs) allow a client to access data from an
untrusted storage device without revealing the access patterns. Typically, the
ORAM adversary can observe both read and write accesses. Write-only ORAMs
target a more practical, {\em multi-snapshot adversary} only monitoring client
writes -- typical for plausible deniability and censorship-resilient systems.
This allows write-only ORAMs to achieve significantly-better asymptotic
performance. However, these apparent gains do not materialize in real
deployments primarily due to the random data placement strategies used to break
correlations between logical and physical namespaces, a required property for
write access privacy. Random access performs poorly on both rotational disks
and SSDs (often increasing wear significantly, and interfering with
wear-leveling mechanisms). In this work, we introduce SqORAM, a new
locality-preserving write-only ORAM that preserves write access privacy without
requiring random data access. Data blocks close to each other in the logical
domain land in close proximity on the physical media. Importantly, SqORAM
maintains this data locality property over time, significantly increasing read
throughput. A full Linux kernel-level implementation of SqORAM is 100x faster
than non locality-preserving solutions for standard workloads and is 60-100%
faster than the state-of-the-art for typical file system workloads
The Cost of Address Translation
Modern computers are not random access machines (RAMs). They have a memory
hierarchy, multiple cores, and virtual memory. In this paper, we address the
computational cost of address translation in virtual memory. Starting point for
our work is the observation that the analysis of some simple algorithms (random
scan of an array, binary search, heapsort) in either the RAM model or the EM
model (external memory model) does not correctly predict growth rates of actual
running times. We propose the VAT model (virtual address translation) to
account for the cost of address translations and analyze the algorithms
mentioned above and others in the model. The predictions agree with the
measurements. We also analyze the VAT-cost of cache-oblivious algorithms.Comment: A extended abstract of this paper was published in the proceedings of
ALENEX13, New Orleans, US
Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency
Recently, several practical attacks raised serious concerns over the security
of searchable encryption. The attacks have brought emphasis on forward privacy,
which is the key concept behind solutions to the adaptive leakage-exploiting
attacks, and will very likely to become mandatory in the design of new
searchable encryption schemes. For a long time, forward privacy implies
inefficiency and thus most existing searchable encryption schemes do not
support it. Very recently, Bost (CCS 2016) showed that forward privacy can be
obtained without inducing a large communication overhead. However, Bost's
scheme is constructed with a relatively inefficient public key cryptographic
primitive, and has a poor I/O performance. Both of the deficiencies
significantly hinder the practical efficiency of the scheme, and prevent it
from scaling to large data settings. To address the problems, we first present
FAST, which achieves forward privacy and the same communication efficiency as
Bost's scheme, but uses only symmetric cryptographic primitives. We then
present FASTIO, which retains all good properties of FAST, and further improves
I/O efficiency. We implemented the two schemes and compared their performance
with Bost's scheme. The experiment results show that both our schemes are
highly efficient, and FASTIO achieves a much better scalability due to its
optimized I/O
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
The -Nearest Neighbor Search (-NNS) is the backbone of several
cloud-based services such as recommender systems, face recognition, and
database search on text and images. In these services, the client sends the
query to the cloud server and receives the response in which case the query and
response are revealed to the service provider. Such data disclosures are
unacceptable in several scenarios due to the sensitivity of data and/or privacy
laws.
In this paper, we introduce SANNS, a system for secure -NNS that keeps
client's query and the search result confidential. SANNS comprises two
protocols: an optimized linear scan and a protocol based on a novel sublinear
time clustering-based algorithm. We prove the security of both protocols in the
standard semi-honest model. The protocols are built upon several
state-of-the-art cryptographic primitives such as lattice-based additively
homomorphic encryption, distributed oblivious RAM, and garbled circuits. We
provide several contributions to each of these primitives which are applicable
to other secure computation tasks. Both of our protocols rely on a new circuit
for the approximate top- selection from numbers that is built from comparators.
We have implemented our proposed system and performed extensive experimental
results on four datasets in two different computation environments,
demonstrating more than faster response time compared to
optimally implemented protocols from the prior work. Moreover, SANNS is the
first work that scales to the database of 10 million entries, pushing the limit
by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202
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
A practical and secure multi-keyword search method over encrypted cloud data
Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective
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
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