High Data Utility Research Articles

Walking Without Friends: Publishing Anonymized Trajectory Dataset Without Leaking Social Relationships

Trajectory data has been widely collected via mobile devices and publicly released for academic research and commercial purposes. One primary concern of publishing such a dataset is the privacy issue. Previous protection schemes mainly focus on preventing re-identification attack, which utilizes the uniqueness of trajectories. However, the correlation between trajectories, which has not been given much attention to before, could also give rise to serious privacy leakage. Recent studies have proved that it is possible to identify social relationship, de-anonymize trajectories or even infer user’s locations by analyzing the correlation between users’ trajectories. We identify the serious privacy problem of social relationship leakage caused by what we call social relationship attack and aim to protect social relationship information, which cannot be protected by existing algorithms. We contribute to the design of a new privacy model and an effective system to deal with social relationship attack and re-identification attack simultaneously while maintaining high data utility. We propose a SlidingWindow algorithm to merge trajectories according to their social-aware distance , which concerns both the spatiotemporal distance and social proximity. Evaluations of two trajectory datasets under different scenarios demonstrate that our system provides more than 1.84 times privacy protection at the cost of only 2.5% data utility loss.

Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis

k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes.This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effective reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes.© 2019 The Authors. Preprint submitted to Elsevier, Inc.

Anomaly detection over differential preserved privacy in online social networks.

The massive reach of social networks (SNs) has hidden their potential concerns, primarily those related to information privacy. Users increasingly rely on social networks for more than merely interactions and self-representation. However, social networking environments are not free of risks. Users are often threatened by privacy breaches, unauthorized access to personal information, and leakage of sensitive data. In this paper, we propose a privacy-preserving model that sanitizes the collection of user information from a social network utilizing restricted local differential privacy (LDP) to save synthetic copies of collected data. This model further uses reconstructed data to classify user activity and detect abnormal network behavior. Our experimental results demonstrate that the proposed method achieves high data utility on the basis of improved privacy preservation. Moreover, LDP sanitized data are suitable for use in subsequent analyses, such as anomaly detection. Anomaly detection on the proposed method’s reconstructed data achieves a detection accuracy similar to that on the original data.

A Differential Privacy-Based Query Model for Sustainable Fog Data Centers

With the increasing computation and storage capabilities of mobile devices, the concept of fog computing was proposed to tackle the high communication delay inherent in cloud computing, and also improve the security to some extent. This paper concerns with the privacy issue inherent in the sustainable fog computing platform. However, there is no universal solution to the privacy problem in fog computing due to the device heterogeneity. In this paper, we proposed a differential privacy-based query model for sustainable fog computing supported data center. We designed a method that can quantify the quality of privacy preserving through rigorous mathematical proof. The proposed method uses the query model to capture the structure information of the sustainable fog computing supported data center, and the datasets for the query result are mapped to real vectors. Then, we implemented the differential privacy preserving by injecting Laplacian noise. The experiment results demonstrated that the proposed method can effectively resist various popular privacy attacks, and achieve relatively high data utility under the premise of better privacy preserving.

Protecting Trajectory From Semantic Attack Considering ${k}$ -Anonymity, ${l}$ -Diversity, and ${t}$ -Closeness

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works is dedicated to merging one’s trajectory with others’, so as to avoid any individual trajectory being re-identified. Yet their solutions do not provide enough protection since they cannot prevent semantic attack, which means the attackers are able to acquire individual’s private information by using the semantics features of frequently visited locations in the trajectory even without re-identification. In this paper, we are the first to recognize the semantic attack, which is another severe privacy problem in publishing trajectory datasets. We propose an algorithm providing strong privacy protection against both the semantic and re-identification attack while reserving high data utility. Extensive evaluations based on two real-world datasets demonstrate that our solution improves the quality of privacy protection by three times, sacrificing only 36% and 10% of spatial and temporal resolution, respectively.

<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math> </inline-formula>-Safe (<inline-formula> <tex-math notation="LaTeX">$l,k$ </tex-math> </inline-formula>)-Diversity Privacy Model for Sequential Publication With High Utility

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The $\tau $ -safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new $\tau $ -safe ( $l,k$ )-diversity privacy model based on generalization and segmentation by record anonymity satisfying $l$ -diversity and individual anonymity satisfying $k$ -anonymity. This privacy model ensures that each record’s signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the $m$ -invariance and $\tau $ -safety model in the sequential publication with arbitrary updates.

HighPU: a high privacy-utility approach to mining frequent itemset with differential privacy

In the field of data mining, frequent itemset mining (FIM) is a popular technique for analysing transaction datasets and establishing the foundation of association rules. Publishing frequent itemsets, however presents privacy challenges. Differential privacy provides strong privacy assurance to users. In this paper, we study the problem of mining frequent itemsets under the rigorous differential privacy model. We propose an approach, called HighPU, which achieves both high data utility and high degree of privacy in FIM. HighPU begins by truncating transactions over the original dataset. Then HighPU directly searches for maximal frequent itemsets. And we use a consistent approach to improve the accuracy of the results. Extensive experiments using several real datasets illustrate that HighPU significantly outperforms the current state of the art.

HighPU: a high privacy-utility approach to mining frequent itemset with differential privacy

In the field of data mining, frequent itemset mining (FIM) is a popular technique for analysing transaction datasets and establishing the foundation of association rules. Publishing frequent itemsets, however presents privacy challenges. Differential privacy provides strong privacy assurance to users. In this paper, we study the problem of mining frequent itemsets under the rigorous differential privacy model. We propose an approach, called HighPU, which achieves both high data utility and high degree of privacy in FIM. HighPU begins by truncating transactions over the original dataset. Then HighPU directly searches for maximal frequent itemsets. And we use a consistent approach to improve the accuracy of the results. Extensive experiments using several real datasets illustrate that HighPU significantly outperforms the current state of the art.

Differential privacy for publishing enterprise-scale WLAN traces

Wireless trace data play an important role in wireless network researches. However, publishing the raw WLAN traces poses potential privacy risks of network users. Therefore, it is necessary to sanitize users’ sensitive information before these traces are published, and provide high data utility for wireless network researches as well. Although some existing works based on various anonymization methods have started to address the problem of sanitizing WLAN traces, we find the anonymization techniques cannot provide strong and provable privacy guarantee by analyzing a real WLAN trace dataset. Differential Privacy is the only framework that can provide strong and provable privacy guarantee. However, our analysis shows that existing studies on differential privacy fail to provide effective data utility for query operations on multi-dimensional and large-scale datasets. Aiming at WLAN trace datasets that have unique characteristics of multi-dimensional and large-scale, this paper proposes a privacy-preserving data publishing algorithm which not only satisfies differential privacy but also realizes high data utility for query operations. We prove that the proposed sanitization algorithm satisfies $$\epsilon $$ -differential privacy. Furthermore, the theoretical analysis shows the noise variance in our sanitization algorithm is $$O(\log ^{o(1)}n/\epsilon ^2)$$ which indicates the algorithm can achieve high data utility on large-scale datasets. Moreover, from the results of extensive experiments on an enterprise-scale WLAN trace dataset, we also show that our sanitization algorithm can provide high data utility for query operations.

A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining.

Mining frequent subgraphs from a collection of input graphs is an important task for exploratory data analysis on graph data. However, if the input graphs contain sensitive information, releasing discovered frequent subgraphs may pose considerable threats to individual privacy. In this paper, we study the problem of frequent subgraph mining (FSM) under the rigorous differential privacy model. We present a two-phase differentially private FSM algorithm, which is referred to as DFG. In DFG, frequent subgraphs are privately identified in the first phase, and the noisy support of each identified frequent subgraph is calculated in the second phase. In particular, to privately identity frequent subgraphs, we propose a frequent subgraph identification approach, which can improve the accuracy of discovered frequent subgraphs through candidate pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support computation approach, which leverages the inclusion relations between the discovered frequent subgraphs to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that DFG satisfies ϵ-differential privacy. Extensive experimental results on real datasets show that DFG can privately find frequent subgraphs while achieving high data utility.

Differentially Private Frequent Sequence Mining.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose PFS2, a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In our PFS2 algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ϵ-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy.

Achieving Probabilistic Anonymity in a Linear and Hybrid Randomization Model

The randomization methods that are applied for privacy-preserving data mining are commonly subject to reconstruction, linkage, and semantic-related attacks. Some existing works employed random noise addition to realize probabilistic anonymity, aiming only at linkage attacks. Random noise addition is vulnerable to reconstruction attacks, and is unable to achieve semantic closeness, particularly on high-dimensional data, to prevent semantic-related attacks. For linkage attacks, the main security vulnerability of their proposed probabilistic anonymity lies in the assumption that the attacker had a priori knowledge of the quasi-identifiers of all individuals. When only some individuals leak their quasi-identifiers, the proposed model will become incapable, because the attacker can deploy a different linkage attack that has not been studied before. This type of attack is much easier to deploy and is thus very harmful. In this paper, we propose new frameworks of probabilistic $(1,k)$ - and $(k,k)$ -anonymity to defend against all these linkage attacks, and realize the frameworks on a hybrid randomization model. The model is also secure against reconstruction attacks. We further achieve statistical semantic closeness of high-dimensional data to prevent semantic-related attacks on the model. The frameworks also allow us to re-design the traditional $K$ -nearest neighbor algorithm to leverage the introduced data uncertainty and improve the mining results. This paper demonstrates the promising applications in large-scale and high-dimensional data mining in clouds, by providing high efficiency and security to protect data privacy, guaranteeing high data utility for mining purposes, on-time processing, and non-interactive data publishing.

Differentially Private Utility Item Mining

In today's business world there is excess of available data & a great need to make good use of it. Data mining is art of extracting pattern and knowledge from large amount of data. Frequent itemset plays essential role in many Data Mining tasks that try to find out interesting patterns from database such as association rules. Association rule mining is a finding association among large databases variables. Mining of frequent itemset is most popular problem in data mining. The frequent itemsets recognition is valuable for economic & research purpose. But valuable discovered frequent itemsets should not only assure security but also achieve high data utility & offer time efficiency. The frequent itemsets are patterns or items like itemset, substructures or subsequences that comes out in data set frequently. There are several Frequent Item mining algorithms for frequent item mining such as Apriori, Frequent Pattern growth, Eclat, Utility Pattern growth algorithms. To provide security or privacy here we use differentially private Utility Item mining algorithm using Utility Pattern- growth algorithm. It consists of Preprocessing & mining phase. In preprocessing phase, to enhance utility & privacy advance smart splitting method is proposed to transform database. For given Database preprocessing phase should be performed only once. In mining phase run time estimation & dynamic reduction performed. To cover the information loss by smart splitting, we contrive run time estimation to calculate actual support of itemsets in original database. For privacy we have added noise in the database, we put forward dynamic reduction method to reduce the noise dynamically which guarantees privacy during mining process. In this paper we proposed new algorithm for mining high utility itemsets called as UP growth which consider not only frequency of itemset but also utility associated with the itemset. Keywords— Frequent Item Mining, є- differential privacy, FP- growth, UP-growth.

An innovative privacy preserving technique for incremental datasets on cloud computing

Cloud computing (CC) is a magnificent service-based delivery with gigantic computer processing power and data storage across connected communications channels. It imparted overwhelming technological impetus in the internet (web) mediated IT industry, where users can easily share private data for further analysis and mining. Furthermore, user affable CC services enable to deploy sundry applications economically. Meanwhile, simple data sharing impelled various phishing attacks and malware assisted security threats. Some privacy sensitive applications like health services on cloud that are built with several economic and operational benefits necessitate enhanced security. Thus, absolute cyberspace security and mitigation against phishing blitz became mandatory to protect overall data privacy. Typically, diverse applications datasets are anonymized with better privacy to owners without providing all secrecy requirements to the newly added records. Some proposed techniques emphasized this issue by re-anonymizing the datasets from the scratch. The utmost privacy protection over incremental datasets on CC is far from being achieved. Certainly, the distribution of huge datasets volume across multiple storage nodes limits the privacy preservation. In this view, we propose a new anonymization technique to attain better privacy protection with high data utility over distributed and incremental datasets on CC. The proficiency of data privacy preservation and improved confidentiality requirements is demonstrated through performance evaluation.

Differentially Private Frequent Subgraph Mining.

Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.

Ant-based routing and QoS-effective data collection for mobile wireless sensor network

Mobility management in mobile wireless sensor networks (MWSNs) is a complex problem that must be taken into account. In MWSN, nodes move in and out of the network randomly. Hence, a path formed between two distant nodes is highly susceptible to changes due to unpredictable node movement. Also, due to the limited resources in WSN, the paths used for data transmission must be tested for the link quality and time consumed for data forwarding. In order to solve these issues, in this paper, an ant-based routing protocol with QoS-effective data collection mechanism is proposed. In this protocol, the link quality and link delay are estimated for each pair of nodes. Link quality is estimated in terms of packet reception rate, received signal strength indicator, and link quality index. A reliable path is chosen from the source to the destination based on the paths traversed by forward ants and backward ants. Then, if the link is found to be defective during data transmission, a link reinforcement technique is used to deliver the data packet at the destination successfully. The mobile robots collect the information with high data utility. In addition, each mobile robot is equipped with multiple antennas, and space division multiple access technique is then applied for effective data collection from multiple mobile robots. Simulation results show that the proposed routing protocol provides reliability by reducing the packet drop and end-to-end delay when compared to the existing protocols.

Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

Abstract Scientific- or public-use files are typically produced by applying anonymisation methods to the original data. Anonymised data should have both low disclosure risk and high data utility. Data utility is often measured by comparing well-known estimates from original data and anonymised data, such as comparing their means, covariances or eigenvalues. However, it is a fact that not every estimate can be preserved. Therefore the aim is to preserve the most important estimates, that is, instead of calculating generally defined utility measures, evaluation on context/data dependent indicators is proposed. In this article we define such indicators and utility measures for the Structure of Earnings Survey (SES) microdata and proper guidelines for selecting indicators and models, and for evaluating the resulting estimates are given. For this purpose, hundreds of publications in journals and from national statistical agencies were reviewed to gain insight into how the SES data are used for research and which indicators are relevant for policy making. Besides the mathematical description of the indicators and a brief description of the most common models applied to SES, four different anonymisation procedures are applied and the resulting indicators and models are compared to those obtained from the unmodified data. The disclosure risk is reported and the data utility is evaluated for each of the anonymised data sets based on the most important indicators and a model which is often used in practice.

Differentially private maximal frequent sequence mining

In this paper, we study the problem of designing a differentially private algorithm for mining maximal frequent sequences, which can not only achieve high data utility and a high degree of privacy, but also provide high time efficiency. To solve this problem, we present a new differentially private algorithm, which is referred to as DP-MFSM. DP-MFSM consists of three phases: pre-processing phase, expected frequent sequence mining (ESM) phase, and candidate extraction and verification (CEV) phase. Specifically, in the pre-processing phase, we first extract some statistical information from the input database, and use the extracted information to determine the values of some variables which will be used in the ESM phase. Then, in the ESM phase, we randomly partition the input database into several sub-databases, and use a partition-based ESM technique to find expected frequent sequences, which are a subset of candidate frequent sequences and more likely to be frequent. At last, in the CEV phase, we extract candidate maximal frequent sequences from the discovered expected frequent sequences, and use a splitting-based technique to verify which candidates are actually frequent in the input database. Through privacy analysis, we show that our DP-MFSM algorithm is ε-differentially private. Extensive experiments on real-world datasets illustrate that our DP-MFSM algorithm can substantially outperform alternative approaches.

Differentially Private Frequent Itemset Mining via Transaction Splitting

Recently, there has been a growing interest in designing differentially private data mining algorithms. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting, we devise a run-time estimation method to estimate the actual support of itemsets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, we show that our PFP-growth algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFP-growth algorithm substantially outperforms the state-of-the-art techniques.

Differentially Private Frequent Sequence Mining via Sampling-based Candidate Pruning.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively reduce the number of unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose a novel differentially private FSM algorithm, which is referred to as PFS2. The core of our algorithm is to utilize sample databases to further prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which sequences are potentially frequent. To improve the accuracy of such private estimations, a sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy.