• ETRI Journal
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• v.35 no.3
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• pp.501-511
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• 2013

Protecting privacy is an important goal in designing location-based services. Service providers want to verify legitimate users and allow permitted users to enjoy their services. Users, however, want to preserve their privacy and prevent tracking. In this paper, a new framework providing users with more privacy and anonymity in both the authentication process and the querying process is proposed. Unlike the designs proposed in previous works, our framework benefits from a combination of three important techniques: k-anonymity, timed fuzzy logic, and a one-way hash function. Modifying and adapting these existing schemes provides us with a simpler, less complex, yet more mature solution. During authentication, the one-way hash function provides users with more privacy by using fingerprints of users' identities. To provide anonymous authentication, the concept of confidence level is adopted with timed fuzzy logic. Regarding location privacy, spatial k-anonymity prevents the users' locations from being tracked. The experiment results and analysis show that our framework can strengthen the protection of anonymity and privacy of users by incurring a minimal implementation cost and can improve functionality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.540-557
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• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.621-624
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• 2013

Mobile Cloud Computing is today's emerging technology. Customers enjoy the services and application from this combination of mobile technology and cloud computing. Beside all these benefits it also increases the concerns regarding privacy of users, while interacting with this new paradigm One of the services is Location based services, but to get their required services user has to give his/her current location to the LBS provider that is violation of location privacy of mobile client. Many approaches are in literature for preserve location privacy but some has computation restriction and some suffer from lack of privacy. In this paper we proposed a novel idea that not only efficient in its protocol but also completely preserves the user's privacy. The result shows that by sharing just service name and a large enough geographic area (e.g. a city) user gets required information from the server by doing little client side processing We perform experiments at client side by developing and testing an android based mobile client application to support our argument.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.411-427
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• 2010

Data sharing is an essential process for collaborative works particularly in the banking, finance and healthcare industries. These industries require many collaborative works with their internal and external parties such as branches, clients, and service providers. When data are shared among collaborators, security and privacy concerns becoming crucial issues and cannot be avoided. Privacy is an important issue that is frequently discussed during the development of collaborative systems. It is closely related with the security issues because each of them can affect the other. The tradeoff between privacy and security is an interesting topic that we are going to address in this paper. In view of the practical problems in the existing approaches, we propose a collaborative framework which can be used to facilitate concurrent operations, single point failure problem, and overcome constraints for two-party computation. Two secure computation protocols will be discussed to demonstrate our collaborative framework.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.286-308
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• 2018

The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network (USN) paradigm. USN provides various types of solutions to the general public in multiple sectors, including environmental monitoring, entertainment, transportation, security, and healthcare. Here, we explore and compare the features of wireless sensor networks and USN. Based on our extensive study, we classify the security- and privacy-related challenges of USNs. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges for designing more secure and privacy-preserving approaches in next-generation USNs.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.169-182
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• 2011

Privacy preserving microdata publication has received wide attention. In this paper, we investigate the randomization approach and focus on attribute disclosure under linking attacks. We give efficient solutions to determine optimal distortion parameters, such that we can maximize utility preservation while still satisfying privacy requirements. We compare our randomization approach with l-diversity and anatomy in terms of utility preservation (under the same privacy requirements) from three aspects (reconstructed distributions, accuracy of answering queries, and preservation of correlations). Our empirical results show that randomization incurs significantly smaller utility loss.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.556-559
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• 2007

Due to its ease of deployment, low cost, self-configuring and self-healing capabilities, Wireless Mesh Networks (WMNs) have emerged as a key technology to be used in a wide scale applications in personal, local, campus, and metropolitan areas. Security and more specifically privacy is an important issue in this type of multi-hop WMN which has given a little attention in the research community. We focus on privacy compromise of a mesh client in a community mesh network that may lead an attacker to reveal mesh clients identity. his other profiles and gain information about mobility. In this paper. we have presented an authentication mechanism with the aid of blind signature that ensures a mesh client to anonymously authenticate itself with a nearby mesh router and thereby preserve identity privacy We have also presented the security and performance analysis of the proposed scheme.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.340-350
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• 2022

The big data term refers to the great volume of data and complicated data structure with difficulties in collecting, storing, processing, and analyzing these data. Big data analytics refers to the operation of disclosing hidden patterns through big data. This information and data set cloud to be useful and provide advanced services. However, analyzing and processing this information could cause revealing and disclosing some sensitive and personal information when the information is contained in applications that are correlated to users such as location-based services, but concerns are diminished if the applications are correlated to general information such as scientific results. In this work, a survey has been done over security and privacy challenges and approaches in big data. The challenges included here are in each of the following areas: privacy, access control, encryption, and authentication in big data. Likewise, the approaches presented here are privacy-preserving approaches in big data, access control approaches in big data, encryption approaches in big data, and authentication approaches in big data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3199-3218
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• 2019

Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user's acceptance of location services in general, and endanger the user's privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker's uncertainty on the user's actual whereabouts by generating a multiple of fake-locations together with user's actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3497-3515
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• 2018

Differential privacy has broadly applied to statistical analysis, and its mainly objective is to ensure the tradeoff between the utility of noise data and the privacy preserving of individual's sensitive information. However, an individual could not achieve expected data utility under differential privacy mechanisms, since the adding noise is random. To this end, we proposed an adaptive Gaussian mechanism based on expected data utility under conditional filtering noise. Firstly, this paper made conditional filtering for Gaussian mechanism noise. Secondly, we defined the expected data utility according to the absolute value of relative error. Finally, we presented an adaptive Gaussian mechanism by combining expected data utility with conditional filtering noise. Through comparative analysis, the adaptive Gaussian mechanism satisfies differential privacy and achieves expected data utility for giving any privacy budget. Furthermore, our scheme is easy extend to engineering implementation.