2,110 research outputs found
Efficient 3-Party Distributed ORAM
Distributed Oblivious RAM (DORAM) protocols---in which parties obliviously access a shared location in a shared array---are a fundamental component of secure-computation protocols in the RAM model. We show here an efficient, 3-party DORAM protocol with semi-honest security for a single corrupted party. To the best of our knowledge, ours is the first protocol for this setting that runs in constant rounds, requires sublinear communication and linear work, and makes only black-box use of cryptographic primitives. We believe our protocol is also concretely more efficient than existing solutions.
As a building block of independent interest, we construct a 3-server distributed point function with security against two colluding servers that is simpler and has better concrete efficiency than prior work
MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture
Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results:
- any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs;
- assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - ? fraction of machines (for an arbitrarily small constant ?) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup.
As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques
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
Communication Complexity and Secure Function Evaluation
We suggest two new methodologies for the design of efficient secure
protocols, that differ with respect to their underlying computational models.
In one methodology we utilize the communication complexity tree (or branching
for f and transform it into a secure protocol. In other words, "any function f
that can be computed using communication complexity c can be can be computed
securely using communication complexity that is polynomial in c and a security
parameter". The second methodology uses the circuit computing f, enhanced with
look-up tables as its underlying computational model. It is possible to
simulate any RAM machine in this model with polylogarithmic blowup. Hence it is
possible to start with a computation of f on a RAM machine and transform it
into a secure protocol.
We show many applications of these new methodologies resulting in protocols
efficient either in communication or in computation. In particular, we
exemplify a protocol for the "millionaires problem", where two participants
want to compare their values but reveal no other information. Our protocol is
more efficient than previously known ones in either communication or
computation
Conclave: secure multi-party computation on big data (extended TR)
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to
run joint computations without revealing private data. Current MPC algorithms
scale poorly with data size, which makes MPC on "big data" prohibitively slow
and inhibits its practical use.
Many relational analytics queries can maintain MPC's end-to-end security
guarantee without using cryptographic MPC techniques for all operations.
Conclave is a query compiler that accelerates such queries by transforming them
into a combination of data-parallel, local cleartext processing and small MPC
steps. When parties trust others with specific subsets of the data, Conclave
applies new hybrid MPC-cleartext protocols to run additional steps outside of
MPC and improve scalability further.
Our Conclave prototype generates code for cleartext processing in Python and
Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave
scales to data sets between three and six orders of magnitude larger than
state-of-the-art MPC frameworks support on their own. Thanks to its hybrid
protocols, Conclave also substantially outperforms SMCQL, the most similar
existing system.Comment: Extended technical report for EuroSys 2019 pape
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
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
In this paper, we propose GraphSE, an encrypted graph database for online
social network services to address massive data breaches. GraphSE preserves
the functionality of social search, a key enabler for quality social network
services, where social search queries are conducted on a large-scale social
graph and meanwhile perform set and computational operations on user-generated
contents. To enable efficient privacy-preserving social search, GraphSE
provides an encrypted structural data model to facilitate parallel and
encrypted graph data access. It is also designed to decompose complex social
search queries into atomic operations and realise them via interchangeable
protocols in a fast and scalable manner. We build GraphSE with various
queries supported in the Facebook graph search engine and implement a
full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that
GraphSE is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE: An
Encrypted Graph Database for Privacy-Preserving Social Search". It includes
the security proof of the proposed scheme. If you want to cite our work,
please cite the conference version of i
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