1,642 research outputs found
Semi-homomorphic Encryption and Multiparty Computation
An additively-homomorphic encryption scheme enables us to compute
linear functions of an encrypted input by manipulating only the ciphertexts. We define the relaxed notion of a semi-homomorphic
encryption scheme, where the plaintext can be recovered as long as the
computed function does not increase the size of the input too
much . We show that a number of existing cryptosystems are
captured by our relaxed notion. In particular, we give examples of semi-homomorphic encryption schemes based on lattices, subset sum and factoring.
We then demonstrate how semi-homomorphic encryption schemes allow us
to construct an efficient multiparty computation protocol for arithmetic circuits, UC-secure against a dishonest majority. The protocol consists of a preprocessing phase and an online phase. Neither the inputs nor the function to be computed have to be known during preprocessing.
Moreover, the online phase is extremely efficient as it requires
no cryptographic operations: the parties only need to exchange additive shares and verify information theoretic MACs.
Our contribution is therefore twofold: from a theoretical point of view, we can base multiparty computation on a variety of different assumptions, while on the practical side we offer a protocol with better efficiency than any previous solution
k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
Data Mining has wide applications in many areas such as banking, medicine,
scientific research and among government agencies. Classification is one of the
commonly used tasks in data mining applications. For the past decade, due to
the rise of various privacy issues, many theoretical and practical solutions to
the classification problem have been proposed under different security models.
However, with the recent popularity of cloud computing, users now have the
opportunity to outsource their data, in encrypted form, as well as the data
mining tasks to the cloud. Since the data on the cloud is in encrypted form,
existing privacy preserving classification techniques are not applicable. In
this paper, we focus on solving the classification problem over encrypted data.
In particular, we propose a secure k-NN classifier over encrypted data in the
cloud. The proposed k-NN protocol protects the confidentiality of the data,
user's input query, and data access patterns. To the best of our knowledge, our
work is the first to develop a secure k-NN classifier over encrypted data under
the semi-honest model. Also, we empirically analyze the efficiency of our
solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text
overlap with arXiv:1307.482
Some Efficient Solutions to Yao's Millionaire Problem
We present three simple and efficient protocol constructions to solve Yao's
Millionaire Problem when the parties involved are non-colluding and
semi-honest. The first construction uses a partially homomorphic Encryption
Scheme and is a 4-round scheme using 2 encryptions, 2 homomorphic circuit
evaluations (subtraction and XOR) and a single decryption. The second
construction uses an untrusted third party and achieves a communication
overhead linear in input bit-size with the help of an order preserving
function.Moreover, the second construction does not require an apriori input
bound and can work on inputs of different bit-sizes. The third construction
does not use a third party and, even though, it has a quadratic communication
overhead, it is a fairly simple construction.Comment: 17 page
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
Separating Two-Round Secure Computation From Oblivious Transfer
We consider the question of minimizing the round complexity of protocols for secure multiparty computation (MPC) with security against an arbitrary number of semi-honest parties. Very recently, Garg and Srinivasan (Eurocrypt 2018) and Benhamouda and Lin (Eurocrypt 2018) constructed such 2-round MPC protocols from minimal assumptions. This was done by showing a round preserving reduction to the task of secure 2-party computation of the oblivious transfer functionality (OT). These constructions made a novel non-black-box use of the underlying OT protocol. The question remained whether this can be done by only making black-box use of 2-round OT. This is of theoretical and potentially also practical value as black-box use of primitives tends to lead to more efficient constructions.
Our main result proves that such a black-box construction is impossible, namely that non-black-box use of OT is necessary. As a corollary, a similar separation holds when starting with any 2-party functionality other than OT.
As a secondary contribution, we prove several additional results that further clarify the landscape of black-box MPC with minimal interaction. In particular, we complement the separation from 2-party functionalities by presenting a complete 4-party functionality, give evidence for the difficulty of ruling out a complete 3-party functionality and for the difficulty of ruling out black-box constructions of 3-round MPC from 2-round OT, and separate a relaxed "non-compact" variant of 2-party homomorphic secret sharing from 2-round OT
Privacy preserving distributed optimization using homomorphic encryption
This paper studies how a system operator and a set of agents securely execute
a distributed projected gradient-based algorithm. In particular, each
participant holds a set of problem coefficients and/or states whose values are
private to the data owner. The concerned problem raises two questions: how to
securely compute given functions; and which functions should be computed in the
first place. For the first question, by using the techniques of homomorphic
encryption, we propose novel algorithms which can achieve secure multiparty
computation with perfect correctness. For the second question, we identify a
class of functions which can be securely computed. The correctness and
computational efficiency of the proposed algorithms are verified by two case
studies of power systems, one on a demand response problem and the other on an
optimal power flow problem.Comment: 24 pages, 5 figures, journa
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