A privacy-preserving method for publishing data with multiple sensitive attributes

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy requirement.Furthermore,sensitive information disclosure may also be caused by these personalised requirements.To address the matter,this article develops a personalised data publishing method for multiple SAs.According to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy guarantees.For the private values,this paper takes the process of anonymisation,while the public values are released without this process.An algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable objects.The experimental results show that the proposed method can provide more information utility when compared with previous methods.The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary.The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

A multiple sensitive attributes data publishing method with guaranteed information utility

Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression ratio.Another category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for analysis.Hence,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility.To acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss.The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes.The proposed method can guarantee information utility when compared with previous ones.

Medical data publishing based on average distribution and clustering

Most of the data publishing methods have not considered sensitivity protection,and hence the adversary can disclose privacy by sensitivity attack.Faced with this problem,this paper presents a medical data publishing method based on sensitivity determination.To protect the sensitivity,the sensitivity of disease information is determined by semantics.To seek the trade-off between information utility and privacy security,the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as possible.The experiments are conducted on two real-world datasets,of which the records include various attributes of patients.To measure sensitivity protection,the authors define a metric,which can evaluate the degree of sensitivity disclosure.Besides,additional information loss and discernability metrics are used to measure the availability of released tables.The experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is guaranteed.Besides value protection,the proposed method can provide sensitivity protection and available releasing for medical data.

Total Domination number of Generalized Petersen Graphs

Generalized Petersen graphs are an important class of commonly used interconnection networks and have been studied . The total domination number of generalized Petersen graphs P(m,2) is obtained in this paper.

SPN-Based Performance Analysis of Multiple Users’Behaviors for SNS

With the rapid development of various applications of Information Technology,big data are increasingly generated by social network services(SNS)nowadays.The designers and providers of SNS distribute different client applications for PC,Mobile phone,IPTV etc.,so that users can obtain related service via mobile or traditional Internet.Good scalability and considerably short time delay are important indices for evaluating social network systems.As a result,investigating and mining the principle of users’behaviors is an important issue which can guide service providers to establish optimal systems with SNS.On the basis of analyzing the characteristics of social network system,this paper constructed a Stochastic Petri Net(SPN)model for describing the behaviors of three users for SNS.Moreover,the scalability of users’behaviors of SNS was studied by extending the SPN model of three users to the one of four users.Furthermore,average time delay was chosen as the performance index to evaluate the performance of these two constructed SPN models with Stochastic Petri Net Package(SPNP)6.0.For different parameters of number of connections,traffic load and buffer size,various trends and numerical results are derived thereby.The methodology of modeling and simulation in this paper can be further used to study the performance of SNS.