Expectation-Maximization Tensor Factorization for Practical Location Privacy Attacks

Murakami, Takao

doi:10.1515/popets-2017-0042

Cited by 18 publications

(22 citation statements)

References 40 publications

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“…Even though map-matching has been used as an attack, for instance, against area obfuscation [21], to the best of our knowledge, this is the first work to consider road-network map-matching as a tracking attack. We also note that this choice was further supported by the fact that hidden Markov chains, which are used in mapmatching, have been shown effective in modelling the temporal correlations of location traces [11,24]. A natural extension of this work is to consider other types of both localization and tracking attack, or even inference attacks, such as the extraction of sensitive semantic locations.…”

Section: Location Privacy Attacksmentioning

confidence: 92%

“…The considered localization attacks assume the space of exact user locations X to be discrete. Therefore, and similarly to previous works [7,10,24], we have discretized the space for both datasets in a grid of equally spaced cells, where the center of the cell corresponds to a locationstamp that is common to any GPS observation within the cell. For the Geolife dataset, the 5 th ring road of Beijing was partitioned in cells of 2000 × 2000 meters for a total of 17 × 16 cells.…”

Section: Localization Attacksmentioning

confidence: 99%

“…An inherent limitation of this finding is the limited number of attacks considered, and specifically, tracking attacks. However, we do note that, the choice of this specific attack was justified by the effectiveness in modelling temporal correlations using hidden Markov models [11,24]. As future work, we would like to expand this work by considering other tracking attacks, such as Kalman filtering.…”

Section: Limitationsmentioning

confidence: 99%

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Impact of Frequency of Location Reports on the Privacy Level of Geo-indistinguishability

Mendes

Cunha

Vilela

2020

Proceedings on Privacy Enhancing Technologies

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Location privacy has became an emerging topic due to the pervasiveness of Location-Based Services (LBSs). When sharing location, a certain degree of privacy can be achieved through the use of Location Privacy-Preserving Mechanisms (LPPMs), in where an obfuscated version of the exact user location is reported instead. However, even obfuscated location reports disclose information which poses a risk to privacy. Based on the formal notion of differential privacy, Geo-indistinguishability has been proposed to design LPPMs that limit the amount of information that is disclosed to a potential adversary observing the reports. While promising, this notion considers reports to be independent from each other, thus discarding the potential threat that arises from exploring the correlation between reports. This assumption might hold for the sporadic release of data, however, there is still no formal nor quantitative boundary between sporadic and continuous reports and thus we argue that the consideration of independence is valid depending on the frequency of reports made by the user. This work intends to fill this research gap through a quantitative evaluation of the impact on the privacy level of Geo-indistinguishability under different frequency of reports. Towards this end, state-of-the-art localization attacks and a tracking attack are implemented against a Geo-indistinguishable LPPM under several values of privacy budget and the privacy level is measured along different frequencies of updates using real mobility data.

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Section: Location Privacy Attacksmentioning

confidence: 92%

Section: Localization Attacksmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

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Impact of Frequency of Location Reports on the Privacy Level of Geo-indistinguishability

Mendes

Cunha

Vilela

2020

Proceedings on Privacy Enhancing Technologies

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“…For example, Montjoye et al [22] put forward that an attacker can reproduce user identity information depending on a small quantity of user location information. Worse still, the location trajectories can be de-anonymized even with sufficient privacy protection, including the noise-fuzzy technology or the anonymity technology [23][24][25]. Specifically, in the presence of prior knowledge of user mobility, an optimal inference attack is available [26], e.g., generating the Markov transition matrix of each user.…”

Section: Related Workmentioning

confidence: 99%

“…Using the resembling cloaking strategy, the proposed spatial and temporal transformations in [31] enforce privacy by hiding mutual proximity. Concerning other realistic scenarios with sparse data and missing location problems, Murakami uses multiple learning methods to resist location privacy attacks based on a Markov chain model [23]. Besides, some LPPMs improve location privacy protection via considering locations semantic.…”

Section: Related Workmentioning

confidence: 99%

Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Tian

Lü

et al. 2021

IEEE Access

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Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k -anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

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Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

et al. 2020

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Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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Expectation-Maximization Tensor Factorization for Practical Location Privacy Attacks

Cited by 18 publications

References 40 publications

Impact of Frequency of Location Reports on the Privacy Level of Geo-indistinguishability

Impact of Frequency of Location Reports on the Privacy Level of Geo-indistinguishability

Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

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