PPSF: An Open-Source Privacy-Preserving and Security Mining Framework

Lin, Jerry Chun‐Wei; Fournier-Viger, Philippe; Wu, Lintai; Gan, Wensheng; Djenouri, Youcef; Zhang, Ji

doi:10.1109/icdmw.2018.00208

Cited by 38 publications

(15 citation statements)

References 17 publications

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“…Recently, many sophisticated tools in the form of open source libraries, frameworks, and prototypes have been developed for data anonymity (DA), privacy preserving data mining (PPDM), and privacy preserving utility mining (PPUM). Lin et al [57] presented a unified privacy preserving and security framework (PPSF) for the DA, PPDP, and PPUM. The developed framework provides thirteen algorithms' implementation.…”

Section: Background and Related Workmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users' data publishing because some attributes may expose multiple users' identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users' communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users' community can occur from the privacy preserved published data. Each item of user attributes impacts users' community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users' unique identifications and sensitive information inferences more easily, and their presence in published data increases users' community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user's community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users' community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user's dataset to effectively preserve users' community privacy. Data generalization is performed adaptively by considering both user attributes' susceptibility and entropy simultaneously. The proposed algorithm controls overgeneralization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users' community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Section: Background and Related Workmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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“…They also realized the phishing of data over the Internet, which arises because data can contain some confidential information. In addition, recent advancements in the field of learning technology have significantly threatened individuals' privacy [9], [15]. Large investments have also been committed to issues on privacy protection, such as privacy preserving data publishing (PPDP) [16] and privacy aware learning [17].…”

Section: Ppdmmentioning

confidence: 99%

“…The practice of extracting patterns (i.e., knowledge) from big data sets is conducted to generate new or useful information, which can be used to represent, interpret, or discover interesting patterns. This practice is referred to as data mining, which is an interdisciplinary subfield of computer science [5]- [9]. The term ''data mining'' has been considered a substitute explanatory term for ''knowledge discovery from data'' (KDD), which is another term that denotes the goal of data mining.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey on Big Data Privacy Protection

et al. 2020

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In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM. INDEX TERMS Security, big data, privacy protection, privacy-preserving data mining.

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“…Finally, it is proved that the proposed scheme improves performance on latency from the viewpoints of mobility and security compared to the existing vertical model. mobile IoT devices connected to private networks in the smart city on the condition that a smart mobility management is provided [10][11][12].In recent decades, preserving privacy and ensuring the security of data have emerged as important issues as confidential information or private data may be revealed by powerful data mining tools [13][14][15]. Therefore, if hackers attack a smart city with lots of IoT devices, the outcome could be far more catastrophic.…”

mentioning

confidence: 99%

“…In recent decades, preserving privacy and ensuring the security of data have emerged as important issues as confidential information or private data may be revealed by powerful data mining tools [13][14][15]. Therefore, if hackers attack a smart city with lots of IoT devices, the outcome could be far more catastrophic.…”

mentioning

confidence: 99%

Integrated Management of Network Address Translation, Mobility and Security on the Blockchain Control Plane

Jung

Agulto

2019

Sensors

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Currently, the dual use of IPv4 and IPv6 is becoming a problem. In particular, Network Address Translation (NAT) is an important issue to be solved because of traversal problems in end-to-end applications for lots of mobile IoT devices connected to different private networks. The vertical model is typically used to solve NAT, mobility and security issues for them. However, the existing vertical model has limitations because it handles NAT, mobility and security management one by one. This paper proposes a Blockchain-based Integrated Network Function Management (BINFM) scheme where the NAT, mobility, and security management are handled at once. The proposed scheme is advantageous in that by using blockchain and the Query/Reply mechanism, each peer can easily obtain the necessary parameters required to handle the NAT, mobility, and security management in a batch. In addition, this paper explains how our proposed scheme guarantees secure end-to-end data transfers with the use of one time session key. Finally, it is proved that the proposed scheme improves performance on latency from the viewpoints of mobility and security compared to the existing vertical model. mobile IoT devices connected to private networks in the smart city on the condition that a smart mobility management is provided [10][11][12].In recent decades, preserving privacy and ensuring the security of data have emerged as important issues as confidential information or private data may be revealed by powerful data mining tools [13][14][15]. Therefore, if hackers attack a smart city with lots of IoT devices, the outcome could be far more catastrophic. It is argued that IPv6 offers better security solutions than IPv4, largely due to IPSec, with which IPv6 operates. It is known that widespread adoption of IPv6 will make man-in-the-middle attacks significantly more difficult [16]. IPSec, which works on a layer 3 plane, i.e., the network layer, aims to provide application-layer security in batch by means of securing IP-layer. However, it poses a difficult problem to run end-to-end encryption because of its difficult key exchange protocol between end-to-end IoT peers [17]. Small IoT end points will face a burden when they handles security association data to secure the layer 3 datagram services. So this paper is based on the idea that IPSec is difficult to be realized for mobile IoT devices with the private IP addresses.As depicted in Figure 1, the existing vertical model starts with the NAT management followed by mobility management. Once the NAT and mobility management are made, the security management procedure begins.Static assignment of IP addresses gives adversaries significant advantage to remotely scan networks and identify their targets accurately and quickly. As traditional approaches against this attack, the IP address assignment scheme based on DHCP or NAT has been used. However, they are insufficient to provide proactive countermeasures because the IP mutation is infrequent and traceable. Recently, OpenFlow Random Host Mutation (OFRHM), in whi...

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PPSF: An Open-Source Privacy-Preserving and Security Mining Framework

Cited by 38 publications

References 17 publications

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Comprehensive Survey on Big Data Privacy Protection

Integrated Management of Network Address Translation, Mobility and Security on the Blockchain Control Plane

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