19,726 research outputs found
Secure Numerical and Logical Multi Party Operations
We derive algorithms for efficient secure numerical and logical operations
using a recently introduced scheme for secure multi-party
computation~\cite{sch15} in the semi-honest model ensuring statistical or
perfect security. To derive our algorithms for trigonometric functions, we use
basic mathematical laws in combination with properties of the additive
encryption scheme in a novel way. For division and logarithm we use a new
approach to compute a Taylor series at a fixed point for all numbers. All our
logical operations such as comparisons and large fan-in AND gates are perfectly
secure. Our empirical evaluation yields speed-ups of more than a factor of 100
for the evaluated operations compared to the state-of-the-art
Privacy-Preserving and Outsourced Multi-User k-Means Clustering
Many techniques for privacy-preserving data mining (PPDM) have been
investigated over the past decade. Often, the entities involved in the data
mining process are end-users or organizations with limited computing and
storage resources. As a result, such entities may want to refrain from
participating in the PPDM process. To overcome this issue and to take many
other benefits of cloud computing, outsourcing PPDM tasks to the cloud
environment has recently gained special attention. We consider the scenario
where n entities outsource their databases (in encrypted format) to the cloud
and ask the cloud to perform the clustering task on their combined data in a
privacy-preserving manner. We term such a process as privacy-preserving and
outsourced distributed clustering (PPODC). In this paper, we propose a novel
and efficient solution to the PPODC problem based on k-means clustering
algorithm. The main novelty of our solution lies in avoiding the secure
division operations required in computing cluster centers altogether through an
efficient transformation technique. Our solution builds the clusters securely
in an iterative fashion and returns the final cluster centers to all entities
when a pre-determined termination condition holds. The proposed solution
protects data confidentiality of all the participating entities under the
standard semi-honest model. To the best of our knowledge, ours is the first
work to discuss and propose a comprehensive solution to the PPODC problem that
incurs negligible cost on the participating entities. We theoretically estimate
both the computation and communication costs of the proposed protocol and also
demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the
outsourcing of costly computations to cloud-computing platforms, as well as to
concerns about privacy of the collected sensitive data. This paper develops a
cloud-based protocol for a quadratic optimization problem involving multiple
parties, each holding information it seeks to maintain private. The protocol is
based on the projected gradient ascent on the Lagrange dual problem and
exploits partially homomorphic encryption and secure multi-party computation
techniques. Using formal cryptographic definitions of indistinguishability, the
protocol is shown to achieve computational privacy, i.e., there is no
computationally efficient algorithm that any involved party can employ to
obtain private information beyond what can be inferred from the party's inputs
and outputs only. In order to reduce the communication complexity of the
proposed protocol, we introduced a variant that achieves this objective at the
expense of weaker privacy guarantees. We discuss in detail the computational
and communication complexity properties of both algorithms theoretically and
also through implementations. We conclude the paper with a discussion on
computational privacy and other notions of privacy such as the non-unique
retrieval of the private information from the protocol outputs
On the Design of Cryptographic Primitives
The main objective of this work is twofold. On the one hand, it gives a brief
overview of the area of two-party cryptographic protocols. On the other hand,
it proposes new schemes and guidelines for improving the practice of robust
protocol design. In order to achieve such a double goal, a tour through the
descriptions of the two main cryptographic primitives is carried out. Within
this survey, some of the most representative algorithms based on the Theory of
Finite Fields are provided and new general schemes and specific algorithms
based on Graph Theory are proposed
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
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