The weight distributions of two classes of p-ary cyclic codes with few weights

Yang, Shudi; Yao, Zheng‐an; Zhao, Chang-An

doi:10.1016/j.ffa.2016.11.004

Cited by 64 publications

(42 citation statements)

References 28 publications

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“…similar worker search, i.e., whether a worker has executed a task. While multi-dimensional application scenarios are more common in practical applications [22][23][24][25][26][27][28][29][30][31][32][33][34] where each dimension is assigned a weight value to indicate the significance [35][36][37][38][39][40][41]. 2.…”

Section: Discussionmentioning

confidence: 99%

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

Yan

2019

J Wireless Com Network

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The ever-increasing popularity of mobile devices (e.g., mobile phones and smart watches) has created a variety of crowdsourcing applications by employing the massive and distributed mobile computing resources. Typically, a task requester sends his/her task request and constraint conditions to a crowdsourcing platform, and then the crowdsourcing platform is responsible for finding a set of appropriate workers (e.g., mobile users) from massive candidates to satisfy the task request. However, for a mobile crowdsourcing task being executed by a set of workers, a pre-selected worker may become unavailable due to various exceptions. In this situation, it is significant for the crowdsourcing platform to quickly find another similar worker to replace the unavailable worker so as to smooth the crowdsourcing process. However, the above exception handling process is often challenging as candidate workers are often not willing to release their sensitive information to the platform due to privacy concerns. In view of this challenge, in this paper, a novel privacy-preserving exception handling approach, named ExH Simhash , is put forward based on Simhash technique. Finally, through a set of simulated experiments, we validate the feasibility of ExH Simhash in terms of substitution equivalence and computational time.

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Section: Discussionmentioning

confidence: 99%

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

Yan

2019

J Wireless Com Network

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“…First of all, we only consider the recommendation scenario where one quality dimension is monitored by sensors, while multi-dimensional and weighted applications are more common in practice [35,36,37], so in the future, we will further refine our work by considering the multiple service quality dimensions as well as their respective weights. Besides, for simplicity, we only discuss the service quality dimensions with real and continuous monitored values, without considering the diversity of the quality values (e.g., discrete [38,39], binary [40], fuzzy [41] and correlated [42,43,44]).…”

Section: Methodsmentioning

confidence: 99%

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Meng

Zhang

et al. 2018

Sensors

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Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

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“…Let p be an odd prime. An [n, k, d] linear code C over F p is a k-dimensional subspace of F n p with minimum (Hamming) distance d. Let A i be the number of codewords of weight i in a linear code C of length n. The numbers A 0 = 1, A 1 , · · · , A n are called the weight distribution of C. We call the polynomial 1+A 1 z+A 2 z 2 +· · ·+A n z n the weight enumerator of C. The weight distribution is a significant research topic in coding theory and it was investigated in [13,14,15,18,19,24,25,26,27,28], as it contains crucial information about the error correcting capability, the probability of error detection and correction with respect to some algorithms.…”

Section: Introductionmentioning

confidence: 99%

A class of linear codes and their complete weight enumerators

Wang¹,

Cao²,

Luo³

2021

Advances in Mathematics of Communications

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Let Fq be the finite field with q = p m elements, where p is an odd prime and m is a positive integer. Let Trm denote the trace function from Fq onto Fp, and the defining set D ⊂ F t q , where t is a positive integer. In this paper, the set D = {(x 1 , x 2 , · · · , xt) ∈ F t q : Trm(x 2 1 + x 2 2 + · · · + x 2 t ) = 0, Trm(x 1 + x 2 + · · · + xt) = 1}. Define the p-ary linear code C D by C D = {c(a 1 , a 2 , · · · , at) : (a 1 , a 2 , · · · , at) ∈ F t q }, where c(a 1 , a 2 , · · · , at) = (Trm(a 1 x 1 + a 1 x 2 · · · + atxt)) (x 1 ,··· ,x t )∈D .We evaluate the complete weight enumerator of the linear codes C D , and present its weight distributions. Some examples are given to illustrate the results.2010 Mathematics Subject Classification: Primary: 94B05; Secondary: 11T71.

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The weight distributions of two classes of p-ary cyclic codes with few weights

Cited by 64 publications

References 28 publications

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

A privacy-preserving exception handling approach for dynamic mobile crowdsourcing applications

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

A class of linear codes and their complete weight enumerators

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