Secure Computation of the $$k^{\mathrm {th}}$$-Ranked Element in a Star Network

Autonomous Orbit Determination using Observations of a Sodium Guide-Star Network

Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) ◽

10.33012/2017.15202 ◽

2017 ◽

Author(s):

Mark L. Psiaki

Keyword(s):

Orbit Determination ◽

Star Network ◽

Autonomous Orbit Determination ◽

Guide Star

Download Full-text

Embedding of Fibonacci Tree into Hyper-Star Network

International Journal of Multimedia and Ubiquitous Engineering ◽

10.21742/ijmue.2018.13.3.01 ◽

2018 ◽

Vol 13 (3) ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Jong-Seok Kim ◽

◽

Hyung Jae Chang ◽

Keyword(s):

Star Network ◽

Fibonacci Tree

Download Full-text

WDM coherent star network with absolute frequency reference

Electronics Letters ◽

10.1049/el:19880892 ◽

1988 ◽

Vol 24 (21) ◽

pp. 1313 ◽

Cited By ~ 23

Author(s):

Y.C. Chung ◽

K.J. Pollock ◽

P.J. Fitzgerald ◽

B. Glance ◽

R.W. Tkach ◽

...

Keyword(s):

Absolute Frequency ◽

Star Network ◽

Frequency Reference

Download Full-text

VPN applications on the Eutelsat D-Star network

10.1049/ic:20030058 ◽

2003 ◽

Author(s):

E. Feltrin

Keyword(s):

Star Network

Download Full-text

Unconditionally Secure Computation with Reduced Interaction

Advances in Cryptology – EUROCRYPT 2016 - Lecture Notes in Computer Science ◽

10.1007/978-3-662-49896-5_15 ◽

2016 ◽

pp. 420-447 ◽

Cited By ~ 4

Author(s):

Ivan Damgård ◽

Jesper Buus Nielsen ◽

Rafail Ostrovsky ◽

Adi Rosén

Keyword(s):

Secure Computation

Download Full-text

Privacy Preserving Deep Learning based on Multi-Party Secure Computation: A Survey

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3058638 ◽

2021 ◽

pp. 1-1

Author(s):

Qiao Zhang ◽

Chunsheng Xin ◽

Hongyi Wu

Keyword(s):

Deep Learning ◽

Privacy Preserving ◽

Secure Computation

Download Full-text

Actively revealing card attack on card-based protocols

Natural Computing ◽

10.1007/s11047-020-09838-8 ◽

2021 ◽

Author(s):

Ken Takashima ◽

Daiki Miyahara ◽

Takaaki Mizuki ◽

Hideaki Sone

Keyword(s):

Secure Computation ◽

Malicious Attack ◽

Formal Framework ◽

Almost All ◽

And Function

AbstractIn 1989, den Boer presented the first card-based protocol, called the “five-card trick,” that securely computes the AND function using a deck of physical cards via a series of actions such as shuffling and turning over cards. This protocol enables a couple to confirm their mutual love without revealing their individual feelings. During such a secure computation protocol, it is important to keep any information about the inputs secret. Almost all existing card-based protocols are secure under the assumption that all players participating in a protocol are semi-honest or covert, i.e., they do not deviate from the protocol if there is a chance that they will be caught when cheating. In this paper, we consider a more malicious attack in which a player as an active adversary can reveal cards illegally without any hesitation. Against such an actively revealing card attack, we define the t-secureness, meaning that no information about the inputs leaks even if at most t cards are revealed illegally. We then actually design t-secure AND protocols. Thus, our contribution is the construction of the first formal framework to handle actively revealing card attacks as well as their countermeasures.

Download Full-text

Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽

10.1155/2018/9653404 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Jibing Wu ◽

Lianfei Yu ◽

Qun Zhang ◽

Peiteng Shi ◽

Lihua Liu ◽

...

Keyword(s):

Real World ◽

Tensor Decomposition ◽

Information Networks ◽

Community Discovery ◽

Star Network ◽

Heterogeneous Information ◽

Heterogeneous Information Networks ◽

General Network ◽

Real World Datasets ◽

Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

Download Full-text