1,439 research outputs found
Geometry-Oblivious FMM for Compressing Dense SPD Matrices
We present GOFMM (geometry-oblivious FMM), a novel method that creates a
hierarchical low-rank approximation, "compression," of an arbitrary dense
symmetric positive definite (SPD) matrix. For many applications, GOFMM enables
an approximate matrix-vector multiplication in or even time,
where is the matrix size. Compression requires storage and work.
In general, our scheme belongs to the family of hierarchical matrix
approximation methods. In particular, it generalizes the fast multipole method
(FMM) to a purely algebraic setting by only requiring the ability to sample
matrix entries. Neither geometric information (i.e., point coordinates) nor
knowledge of how the matrix entries have been generated is required, thus the
term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme
for hierarchical matrix computations that reduces synchronization barriers. We
present results on the Intel Knights Landing and Haswell architectures, and on
the NVIDIA Pascal architecture for a variety of matrices.Comment: 13 pages, accepted by SC'1
DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization
Recent research has demonstrated that Intel's SGX is vulnerable to various
software-based side-channel attacks. In particular, attacks that monitor CPU
caches shared between the victim enclave and untrusted software enable accurate
leakage of secret enclave data. Known defenses assume developer assistance,
require hardware changes, impose high overhead, or prevent only some of the
known attacks. In this paper we propose data location randomization as a novel
defensive approach to address the threat of side-channel attacks. Our main goal
is to break the link between the cache observations by the privileged adversary
and the actual data accesses by the victim. We design and implement a
compiler-based tool called DR.SGX that instruments enclave code such that data
locations are permuted at the granularity of cache lines. We realize the
permutation with the CPU's cryptographic hardware-acceleration units providing
secure randomization. To prevent correlation of repeated memory accesses we
continuously re-randomize all enclave data during execution. Our solution
effectively protects many (but not all) enclaves from cache attacks and
provides a complementary enclave hardening technique that is especially useful
against unpredictable information leakage
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
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