This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the sce...This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involving multiparty network data sharing with Trusted Third Party (TTP) is proposed. Simulations are then conducted using network data from different sources, and show that the measurement indicators defined in this paper can adequately quantify the privacy of the network. In particular, it can indicate the effect of the auxiliary information of the adversary on privacy.展开更多
An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capa...An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capacity.However,the outsource database still has some challenges.If the service provider does not have sufficient confidence,there is the possibility of data leakage.The data may has user＇s privacy,so data leakage may cause data privacy leak.Based on this factor,to protect the privacy of data in the outsource database becomes very important.In the past,scholars have proposed k-anonymity to protect data privacy in the database.It lets data become anonymous to avoid data privacy leak.But k-anonymity has some problems,it is irreversible,and easier to be attacked by homogeneity attack and background knowledge attack.Later on,scholars have proposed some studies to solve homogeneity attack and background knowledge attack.But their studies still cannot recover back to the original data.In this paper,we propose a data anonymity method.It can be reversible and also prevent those two attacks.Our study is based on the proposed r-transform.It can be used on the numeric type of attributes in the outsource database.In the experiment,we discussed the time required to anonymize and recover data.Furthermore,we investigated the defense against homogeneous attack and background knowledge attack.At the end,we summarized the proposed method and future researches.展开更多
基金supported by the National Key Basic Research Program of China (973 Program) under Grant No. 2009CB320505the Fundamental Research Funds for the Central Universities under Grant No. 2011RC0508+2 种基金the National Natural Science Foundation of China under Grant No. 61003282China Next Generation Internet Project "Research and Trial on Evolving Next Generation Network Intelligence Capability Enhancement"the National Science and Technology Major Project "Research about Architecture of Mobile Internet" under Grant No. 2011ZX03002-001-01
文摘This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involving multiparty network data sharing with Trusted Third Party (TTP) is proposed. Simulations are then conducted using network data from different sources, and show that the measurement indicators defined in this paper can adequately quantify the privacy of the network. In particular, it can indicate the effect of the auxiliary information of the adversary on privacy.
文摘An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capacity.However,the outsource database still has some challenges.If the service provider does not have sufficient confidence,there is the possibility of data leakage.The data may has user＇s privacy,so data leakage may cause data privacy leak.Based on this factor,to protect the privacy of data in the outsource database becomes very important.In the past,scholars have proposed k-anonymity to protect data privacy in the database.It lets data become anonymous to avoid data privacy leak.But k-anonymity has some problems,it is irreversible,and easier to be attacked by homogeneity attack and background knowledge attack.Later on,scholars have proposed some studies to solve homogeneity attack and background knowledge attack.But their studies still cannot recover back to the original data.In this paper,we propose a data anonymity method.It can be reversible and also prevent those two attacks.Our study is based on the proposed r-transform.It can be used on the numeric type of attributes in the outsource database.In the experiment,we discussed the time required to anonymize and recover data.Furthermore,we investigated the defense against homogeneous attack and background knowledge attack.At the end,we summarized the proposed method and future researches.
