4,983 research outputs found
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
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
Compositional closure for Bayes Risk in probabilistic noninterference
We give a sequential model for noninterference security including probability
(but not demonic choice), thus supporting reasoning about the likelihood that
high-security values might be revealed by observations of low-security
activity. Our novel methodological contribution is the definition of a
refinement order and its use to compare security measures between
specifications and (their supposed) implementations. This contrasts with the
more common practice of evaluating the security of individual programs in
isolation.
The appropriateness of our model and order is supported by our showing that
our refinement order is the greatest compositional relation --the compositional
closure-- with respect to our semantics and an "elementary" order based on
Bayes Risk --- a security measure already in widespread use. We also relate
refinement to other measures such as Shannon Entropy.
By applying the approach to a non-trivial example, the anonymous-majority
Three-Judges protocol, we demonstrate by example that correctness arguments can
be simplified by the sort of layered developments --through levels of
increasing detail-- that are allowed and encouraged by compositional semantics
Secure multiparty quantum computation for summation and multiplication
As a fundamental primitive, Secure Multiparty Summation and Multiplication can be used to build complex secure protocols for other multiparty computations, specially, numerical computations. However, there is still lack of systematical and efficient quantum methods to compute Secure Multiparty Summation and Multiplication. In this paper, we present a novel and efficient quantum approach to securely compute the summation and multiplication of multiparty private inputs, respectively. Compared to classical solutions, our proposed approach can ensure the unconditional security and the perfect privacy protection based on the physical principle of quantum mechanics
Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation
We propose a privacy-preserving Naive Bayes classifier and apply it to the
problem of private text classification. In this setting, a party (Alice) holds
a text message, while another party (Bob) holds a classifier. At the end of the
protocol, Alice will only learn the result of the classifier applied to her
text input and Bob learns nothing. Our solution is based on Secure Multiparty
Computation (SMC). Our Rust implementation provides a fast and secure solution
for the classification of unstructured text. Applying our solution to the case
of spam detection (the solution is generic, and can be used in any other
scenario in which the Naive Bayes classifier can be employed), we can classify
an SMS as spam or ham in less than 340ms in the case where the dictionary size
of Bob's model includes all words (n = 5200) and Alice's SMS has at most m =
160 unigrams. In the case with n = 369 and m = 8 (the average of a spam SMS in
the database), our solution takes only 21ms
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