Outsnurcing computing allows users with resource-constrained devices tn outsnurce their complex computation wnrkloads to cloud servers that may not be honest. In this paper, we propose a new algorithm for securing the...Outsnurcing computing allows users with resource-constrained devices tn outsnurce their complex computation wnrkloads to cloud servers that may not be honest. In this paper, we propose a new algorithm for securing the outsourcing of composite modnlar exponentiation, which is one of the most complex computing tasks in discrete- log based cryptographic protocols. Unlike algorithms based on two untrusted servers, we outsnurce modular expnnentiation operation to only a single server, which eliminates the potential for a cnllusinn attack when using two servers. Moreover, our proposed algorithm can hide the base and exponent of the outsourced data, which prevents the exposure of sensitive information to clnud servers. In addition, compared with the state-of-the-art algorithms, our scheme has remarkably better checkability, The user could detect any misbehavior with a probability of one if the server returns a fault result.展开更多
With the development of information technology,a mass of data are generated every day.Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market compet...With the development of information technology,a mass of data are generated every day.Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real life.However,these analyses are based on users’data,which disclose users’privacy.Local differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering analysis.However,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy guarantee.To solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering analysis.Instead of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each other.To be specific,we generate a noisy distance first and then synthesize the high-dimensional data record.We propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,respectively.Furthermore,we introduce a way to generate synthetic data records in high-dimensional space.Our experimental evaluation results show that our methods outperform the traditional methods significantly.展开更多
文摘Outsnurcing computing allows users with resource-constrained devices tn outsnurce their complex computation wnrkloads to cloud servers that may not be honest. In this paper, we propose a new algorithm for securing the outsourcing of composite modnlar exponentiation, which is one of the most complex computing tasks in discrete- log based cryptographic protocols. Unlike algorithms based on two untrusted servers, we outsnurce modular expnnentiation operation to only a single server, which eliminates the potential for a cnllusinn attack when using two servers. Moreover, our proposed algorithm can hide the base and exponent of the outsourced data, which prevents the exposure of sensitive information to clnud servers. In addition, compared with the state-of-the-art algorithms, our scheme has remarkably better checkability, The user could detect any misbehavior with a probability of one if the server returns a fault result.
文摘With the development of information technology,a mass of data are generated every day.Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real life.However,these analyses are based on users’data,which disclose users’privacy.Local differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering analysis.However,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy guarantee.To solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering analysis.Instead of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each other.To be specific,we generate a noisy distance first and then synthesize the high-dimensional data record.We propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,respectively.Furthermore,we introduce a way to generate synthetic data records in high-dimensional space.Our experimental evaluation results show that our methods outperform the traditional methods significantly.
