Frequent Itemset Mining of User’s Multi-attribute Under Local Differential Privacy

Liu, Haijiang; Cui, Lianwei; Ma, Xuebin; Wu, Celimuge

doi:10.32604/cmc.2020.010987

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…In the future, trajectory generation is a hot research field. There are still many problems to be solved, such as (1) we should solve the geographic data sparsity to improve the availability of the trained model; (2) we need to improve the efficiency and effectiveness of high-dimensional sequence execution; (3) we need more measures for the availability of the trajectory data; (4) we should introduce stricter privacy mechanism in trajectory generation, such differential privacy; (5) we should eliminate some privacy threats, such as Bayesian inference threat, partial sniffing threat, and outlier leakage threat.…”

Section: Discussionmentioning

confidence: 99%

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Generating Synthetic Trajectory Data Using GRU

Liu¹,

Cui²,

Xing³

2022

Intelligent Automation &Amp; Soft Computing

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With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users' trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users' privacy, such as home addresses, medical records, and other living habits. That promotes the development of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of generation. The prediction of the following position in the trajectory generation is very dependent on the historical location data, but the relationship between trajectory positions tends to be ignored. Most commonly used methods only adopt the probability distribution of users' positions to generate synthetic data. On the one hand, this type of statistical method is too rough, and on the other hand, it cannot bring more benefits in availability by increasing data volume. We propose a new trajectory generation method in this paper-Trajectory Generation Model with RNNs(TGMRNN), to address the deficiencies above. It adopts the RNN model to replace the traditional Markov model to generate trajectory data with higher availability. Meanwhile, it solves the problem that RNNs are unsuitable for continuous location data by representing trajectories as discretized data with the grid method. We have conducted experiments in a real data set. Compared with the Markov model, the results of TGMRNN demonstrate that it is superior to some existing methods.

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Section: Discussionmentioning

confidence: 99%

“…Trajectory generators can well solve the above problems. Namely, on the premise of guaranteeing the users' location privacy, it can publish the highly available synthetic trajectory data [4][5][6]. The trajectory generators extract features and model the moving pattern of users' to train the model.…”

Section: Introductionmentioning

confidence: 99%

Generating Synthetic Trajectory Data Using GRU

Liu¹,

Cui²,

Xing³

2022

Intelligent Automation &Amp; Soft Computing

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“…Frequent itemsets (FIs) can be mined from transaction databases through one of the traditional algorithms that can be generally grouped into two methods [3]: Apriori-based method, which is used for generating and filtering candidate itemsets such as Apriori algorithm [4], and tree-based method that is normally used for building FP-tree and then mining FIs from the FP-tree such as FP-Growth [5], TRR [6], PrePost+ [7], FIN [8], dFIN [9], and negFIN [10] algorithms. Since Apriori-based methods depend on continuous scanning of the database to generate multiple candidate itemsets, they require high I/O.…”

Section: Introductionmentioning

confidence: 99%

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

Alsaeedi

Al-Hegami

2022

Complexity

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Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.

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“…This topic has attracted a lot of research attention, and many methods have been proposed, such as dEclat [1], FP-Growth * [2], DBV-FI (Dynamic Bit Vector for mining Frequent Itemsets) [3], and NSFI (N-list and Subsume-based algorithm for mining Frequent Itemsets) [4]. Frequent itemsets have also been applied to solvethe problem of multi-attribute users under conditions of local differential privacy [5]. There are also numerous variations of pattern mining, including frequent closed itemset mining [6], maximal frequent patterns [7], frequent weighted itemset mining [8], utility patterns [9,10], colossal patterns [11], erasable itemset mining [12], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

Nguyen¹,

Le²

2022

Computers, Materials &Amp; Continua

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The task of mining erasable patterns (EPs) is a data mining problem that can help factory managers come up with the best product plans for the future. This problem has been studied by many scientists in recent times, and many approaches for mining EPs have been proposed. Erasable closed patterns (ECPs) are an abbreviated representation of EPs and can be considered condensed representations of EPs without information loss. Current methods of mining ECPs identify huge numbers of such patterns, whereas intelligent systems only need a small number. A ranking process therefore needs to be applied prior to use, which causes a reduction in efficiency. To overcome this limitation, this study presents a robust method for mining toprank-k ECPs in which the mining and ranking phases are combined into a single step. First, we propose a virtual-threshold-based pruning strategy to improve the mining speed. Based on this strategy and dPidset structure, we then develop a fast algorithm for mining top-rank-k ECPs, which we call TRK-ECP. Finally, we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept), which are state-of-the-art algorithms for mining top-rank-k EPs. The results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.

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Frequent Itemset Mining of User’s Multi-attribute Under Local Differential Privacy

Cited by 4 publications

References 5 publications

Generating Synthetic Trajectory Data Using GRU

Generating Synthetic Trajectory Data Using GRU

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

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