• Title/Summary/Keyword: Privacy-preserving

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Whale Optimization Algorithm and Blockchain Technology for Intelligent Networks

  • Sulthana, Shazia;Reddy, BN Manjunatha
    • International Journal of Computer Science & Network Security
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    • v.22 no.7
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    • pp.157-164
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    • 2022
  • The proposed privacy preserving scheme has identified the drawbacks of existing schemes in Vehicular Networks. This prototype enhances the number of nodes by decreasing the cluster size. This algorithm is integrated with the whale optimization algorithm and Block Chain Technology. A set of results are done through the NS-2 simulator in the direction to check the effectiveness of proposed algorithm. The proposed method shows better results than with the existing techniques in terms of Delay, Drop, Delivery ratio, Overhead, throughout under the denial of attack.

Preserving User Anonymity in Context-Aware Location-Based Services: A Proposed Framework

  • Teerakanok, Songpon;Vorakulpipat, Chalee;Kamolphiwong, Sinchai;Siwamogsatham, Siwaruk
    • 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.

Privacy Protection Method for Sensitive Weighted Edges in Social Networks

  • Gong, Weihua;Jin, Rong;Li, Yanjun;Yang, Lianghuai;Mei, Jianping
    • 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.

An Appraoch for Preserving Loaction Privacy using Location Based Services in Mobile Cloud Computing

  • Abbas, Fizza;Hussain, Rasheed;Son, Junggab;Oh, Heekuck
    • 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.

Clustering-Based Federated Learning for Enhancing Data Privacy in Internet of Vehicles

  • Zilong Jin;Jin Wang;Lejun Zhang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.6
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    • pp.1462-1477
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    • 2024
  • With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

Semi-trusted Collaborative Framework for Multi-party Computation

  • Wong, Kok-Seng;Kim, Myung-Ho
    • 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.

Security and Privacy in Ubiquitous Sensor Networks

  • Perez, Alfredo J.;Zeadally, Sherali;Jabeur, Nafaa
    • 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.

Limiting Attribute Disclosure in Randomization Based Microdata Release

  • Guo, Ling;Ying, Xiaowei;Wu, Xintao
    • 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.

A Privacy Preserving Authentication Mechanism for Wireless Mesh Networks

  • Islam, Shariful;Hamid, Abdul;Hong, Choong-Seon
    • 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.

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Big Data Key Challenges

  • Alotaibi, Sultan
    • 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.