Toward inference attacks for k-anonymity

Sun, Yan; Liu, Yin; Liu, Licai; Xin, Shuang

doi:10.1007/s00779-014-0787-y

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Therefore, homogeneity attacks are possible when users cloaked together query the same service. This makes it possible for an adversary to link the query to these users [4,25].…”

Section: Homogeneity Attackmentioning

confidence: 99%

“…The steps will be repeated until the size of S i doesn't change (steps [22][23][24]. The MBR r i covering S i can be a candidate cloaking region (steps [25][26]. Finally, the privacy model δp is calculated.…”

Section: Algorithm Depictionmentioning

confidence: 99%

“…An adversary with the following knowledge can launch an attack; Attacking model Having these pieces of information an attacker may be able to launch three types of attacks namely homogeneity attack [25], query tracking attack [9] and maximum movement boundary (MMB) attack [15].…”

Section: Security Analysismentioning

confidence: 99%

See 2 more Smart Citations

Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Memon

2015

Wireless Pers Commun

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Location based services (LBSs) are gaining importance due to the advancement in mobile networks and positioning technologies. The proliferation of location-based services in recent years has highlighted the need to consider location privacy. This has led to the development of methods enhancing location privacy, and to the investigation of reasons for sharing location information. While computational attacks on location privacy and their prevention have attracted a lot of research, attacks based on humans strategies and tactics have mostly been considered implicitly. Nevertheless, in querying LBSs, a user send its exact locations to their location service providers but in the process, location information of the users are misused either purposefully or otherwise by service providers creating privacy issues for users. It has therefore become important that mechanisms necessary to protect the privacy of users are adopted when querying location based services. It's on this premise that we introduced a novel query privacy algorithm called the authentication speed dynamic transportation mode cloaking algorithm for continuous query LBSs that considers users' similarity in speed, direction and travelling with the same transport mode for cloaking for anonymization. Experimental evaluation of the algorithm on a real world map shows that our model ensures total privacy for users, enhanced privacy guarantee, improves quality of service significantly and achieved an excellent performance measure also we compare our method with existing privacy protection methods such as V-DCA, DSDCA, AVD-DCA,D-TC and GCA.

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“…Therefore, homogeneity attacks are possible when users cloaked together query the same service. This makes it possible for an adversary to link the query to these users [4,25].…”

Section: Homogeneity Attackmentioning

confidence: 99%

Section: Algorithm Depictionmentioning

confidence: 99%

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Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Memon

2015

Wireless Pers Commun

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“…The attackers can use this information to infer the user's travel patterns, hobbies and interests, and other personal privacy information. Location privacy threat refers to, under unauthorized circumstance, the fact that attacker tracks the original position information through location device and technology and infers the privacy information related to user location through reasoning [12]. Location privacy protection method mainly refers to the fact that the user provides false user location privacy information or anonymous user's identity information and location information to the server in the process of location service.…”

Section: Related Workmentioning

confidence: 99%

Location Privacy Protection Based on ImprovedK-Value Method in Augmented Reality on Mobile Devices

Yin

Sun

2017

Mobile Information Systems

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With the development of Augmented Reality technology, the application of location based service (LBS) is more and more popular, which provides enormous convenience to people’s life. User location information could be obtained at anytime and anywhere. So user location privacy security suffers huge threats. Therefore, it is crucial to pay attention to location privacy protection in LBS. Based on the architecture of the trusted third party (TTP), we analyzed the advantages and shortages of existing location privacy protection methods in LBS on mobile terminal. Then we proposed the improvedK-value location privacy protection method according to privacy level, which combinesk-anonymity method with pseudonym method. Through the simulation experiment, the results show that this improved method can anonymize all service requests effectively. In addition to the experiment of execution time, it demonstrated that our proposed method can realize the location privacy protection more efficiently.

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“…Q-IDs are attributes that contain private information, where adversaries can use to unveil hidden data by linking them to related external elements [7]. Current anonymization algorithms aim to improve data privacy by generalizing the Q-ID attributes through processes that utilize taxonomy tree, interval, or suppression.…”

mentioning

confidence: 99%

Improving MapReduce privacy by implementing multi-dimensional sensitivity-based anonymization

2017

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Toward inference attacks for k-anonymity

Cited by 13 publications

References 14 publications

Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Authentication User’s Privacy: An Integrating Location Privacy Protection Algorithm for Secure Moving Objects in Location Based Services

Location Privacy Protection Based on ImprovedK-Value Method in Augmented Reality on Mobile Devices

Improving MapReduce privacy by implementing multi-dimensional sensitivity-based anonymization

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