Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy preservation.The enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold approach.The threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

基于事务型K-Anonymity的动态集值属性数据重发布隐私保护方法

研究了动态集值属性数据重发布中的隐私保护问题.真实的数据随时间的推移因插入、删除、修改等操作而产生动态变化.更新后数据的重发布将面临攻击者使用历史发布结果对敏感信息揭露的风险.提出了一种面向动态集值属性数据重发布的隐私保护模型,延续使用事务型k-anonymity原则保护记录间的不可区分性,并通过维持记录中敏感元素在更新过程中的多样性和连续性阻止其被揭露.结合局部重编码泛化和隐匿技术降低数据匿名产生的信息损失,进而提出了完整的重发布算法.通过在真实数据集上进行的实验和比较,研究结果表明提出的方法能有效阻止敏感信息的泄露,并降低发布结果的信息损失.

一种基于K-anonymity模型的数据隐私保护算法

在数据共享的同时,如何保证数据的隐私性是一个重要的问题。泛化方法是数据隐私保护的一种重要方法,但现有的泛化算法不能处理连续属性,数据错误率比较高。在K-anonymity模型基础上,提出了一种扩展泛化算法EGA(Extended Generalization Algorithm),该算法在满足给定K值的条件下,用相对不具体的值最小限度地替换敏感数据,并实现了对离散属性和连续属性的处理。实验结果表明,与现有泛化算法相比,提出的算法具有运行效率高、数据错误率低、能保持敏感数据分类特性等优点。

Reciprocal Cloaking Algorithm for Spatial K-Anonymity

Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. Due to the nature of spatial queries, location-based service (LBS) needs the user position in order to process requests. On the other hand, revealing exact user locations to LBS may pinpoint their identities and breach their privacy. Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. However, existing reciprocal methods rely on a specialized data structure. In contrast, a reciprocal algorithm was proposed using existing spatial index on the user locations. At the same time, an adjusted median splits algorithm was provided. Finally, according to effectiveness (i.e., anonymizing spatial region size) and efficiency (i.e., construction cost), the experimental results verify that the proposed methods have better performance. Moreover, since using employ general-purpose spatial indices, the proposed method supports conventional spatial queries as well.

An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

Research on k-anonymity privacy protection scheme based on bilinear pairings

Aimed at enhancing privacy protection of location-based services（ LBS） in mobile Internet environment,an improved privacy scheme of high service quality on the basis of bilinear pairings theory and k-anonymity is proposed. In circular region of Euclidian distance,mobile terminal evenly generates some false locations,from which half optimal false locations are screened out according to position entropy,location and mapping background information. The anonymity obtains the effective guarantee,so as to realize privacy protection. Through security analyses,the scheme is proved not only to be able to realize such security features as privacy,anonymity and nonforgeability,but also able to resist query tracing attack. And the result of simulation shows that this scheme not only has better evenness in selecting false locations,but also improves efficiency in generating and selecting false nodes.

A Trajectory Privacy Protection Method to Resist Long-Term Observation Attacks

Users face the threat of trajectory privacy leakage when using location-based service applications, especially when their behavior is collected and stored for a long period of time. This accumulated information is exploited by opponents, greatly increasing the risk of trajectory privacy leakage. This attack method is called a long-term observation attack. On the premise of ensuring lower time overhead and higher cache contribution rate, the existing methods cannot utilize cache to answer subsequent queries while also resisting long-term observation attacks. So this article proposes a trajectory privacy protection method to resist long-term observation attacks. This method combines caching technology and improves the existing differential privacy mechanism, while incorporating randomization factors that are difficult for attackers to recognize after long-term observation to enhance privacy. Search for locations in the cache of both the mobile client and edge server that can replace the user’s actual location. If there are replacement users in the cache, the query results can be obtained more quickly. Simultaneously obfuscating the spatiotemporal correlation of actual trajectories by generating confusion regions. If it does not exist, the obfuscated location generation method that resists long-term observation attacks is executed to generate the real anonymous area and send it to the service provider. The above steps can comprehensively protect the user’s trajectory privacy. The experimental results show that this method can protect user trajectories from long-term observation attacks while ensuring low time overhead and a high cache contribution rate.

A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve.Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area.When existing approaches are investigated,one of the most significant difficulties discovered is the presence of outlier data in the datasets.Outlier data has a negative impact on data utility.Furthermore,k-anonymity algorithms,which are commonly used in the literature,do not provide adequate protection against outlier data.In this study,a new data anonymization algorithm is devised and tested for boosting data utility by incorporating an outlier data detection mechanism into the Mondrian algorithm.The connectivity-based outlier factor(COF)algorithm is used to detect outliers.Mondrian is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world data.COF,on the other hand,is used to discover outliers in high-dimensional datasets with complicated structures.The proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on k-anonymization.In addition,it outperforms other algorithms in the discernibility metric(DM),normalized average equivalence class size(Cavg),global certainty penalty(GCP),query error rate,classification accuracy(CA),and F-measure metrics.Moreover,the increase in the values of theGCPand error ratemetrics demonstrates that the proposed algorithm facilitates obtaining higher data utility by grouping closer data points when compared to other algorithms.

一种保护链接关系的分布式匿名算法

随着在线社会网络的发展和普及,社会网络用户呈爆炸式增长,面对海量的社会网络数据,传统的隐私保护技术已不能满足实际需求.因此,提出一种能够抵抗节点重识别攻击和边泄露的分布式社会网络隐私保护方法 DAPLR(Distributed Anonymous Protecting Link Relationships),该方法基于分布式图处理系统Graph X编程模式遵循"节点为中心"的特点,通过节点间的消息传递将互为N-hop邻居的节点分为一组,有效地保护了节点的链接关系,然后利用Graph X对分组中节点进行标签kanonymity.实验表明,DAPLR方法提高了处理大规模社会网络数据的效率,发布的匿名数据具有很好的可用性.

QR-TCM:具有质量保证的位置服务隐私保护模型

针对传统的基于位置服务的隐私模型匿名时间较长的情况,建立了QR-TCM模型。该模型提出了隐私保护算法CRCA。通过分析影响匿名时间的因素,提出了解决用户服务延迟的方法以及位置服务质量评价模型。实验采用了标准数据集上的数据,通过响应时间、隐私性等多个维度去衡量QR-TCM模型。实验结果证明,该方法适用于连续查询位置隐私保护,可有效保护用户的位置隐私和提供及时的服务。

一种移动Ad hoc环境下的LBS位置保护算法的研究

传统的基于位置信息的服务(LBS)的隐私保护需要LBS提供者(简称LSP)与用户之间通过第三方作为中介来进行信息交换,但这种模式极易遭到攻击者攻击。为此提出一种基于K-匿名机制的隐形空间算法KABSCA(k-anonymity based spatial cloaking algorithm),通过移动设备独立建立一个分布式网络直接与LSP通讯进而避免了第三方的安全威胁。仿真实验显示:使用这种算法,用户可以享受到高质量的信息服务以及高度的隐私保护。

稀疏环境下基于假轨迹的轨迹隐私保护方法

针对稀疏环境下的移动对象轨迹数据经匿名处理后可用性低的问题,提出一种稀疏环境下基于假轨迹的轨迹隐私保护算法。在本文算法中,考虑了移动对象所处的地理环境,将轨迹的整体方向和轨迹间距作为选择假轨迹的重要依据。此外,还提出了使用访问概率的概念来平衡匿名和数据可用性,从而实现轨迹数据匿名。基于移动对象的轨迹数据集进行实验与分析,实验结果表明,本文算法在满足轨迹数据匿名需求的情况下有更高的数据可用性。

Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.

一种医疗数据发布匿名化模型

针对疾病这个敏感属性包含两重语义信息的特点,提出了一种(w,k,d)-匿名模型。该模型首先对疾病的敏感等级进行划分,计算每种疾病所在分级的权重值,限制每个等价类的平均权重值不大于给定的约束值w;其次,按照语义层次树对疾病进行划分,要求等价类的平均语义层次距离不小于给定的约束值d,最终实现对于疾病这个敏感属性的个性化保护。实验数据表明,尽管消耗了一些执行时间,但是却能更有效地阻止疾病属性被相似性攻击,保护病人隐私。

Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

Privacy preserving data mining (PPDM) has become more and more important because it allows sharing of privacy sensitive data for analytical purposes. A big number of privacy techniques were developed most of which used the k-anonymity property which have many shortcomings, so other privacy techniques were introduced (l-diversity, p-sensitive k-anonymity, (α, k)-anonymity, t-closeness, etc.). While they are different in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data. This paper is concerned with providing an enhanced privacy technique that combines some anonymity techniques to maintain both privacy and data utility by considering the sensitivity values of attributes in queries using sensitivity weights which determine taking in account utility-based anonymization and then only queries having sensitive attributes whose values exceed threshold are to be changed using generalization boundaries. The threshold value is calculated depending on the different weights assigned to individual attributes which take into account the utility of each attribute and those particular attributes whose total weights exceed the threshold values is changed using generalization boundaries and the other queries can be directly published. Experiment results using UT dallas anonymization toolbox on real data set adult database from the UC machine learning repository show that although the proposed technique preserves privacy, it also can maintain the utility of the publishing data.

A Generalized Location Privacy Protection Scheme in Location Based Services

When the user getting location based services by the traditional technology,his location information of region is always be exposed.However,in modern mobile networks,even the current geographical region is a part of privacy information.To solve this problem,a new generalized k-anonymity location privacy protection scheme in location based services(LPPS-GKA)with the third trust servicer is proposed.And it can guarantee the users get good location-based services(LBS)without leaking the information of the geo-location region,which has protected the perfect privacy.Analysis shows that LPPS-GKA is more secure in protecting location privacy,including region information,and is more efficient than other similar schemes in computational and communicational aspects.It is suitable for dynamic environment for different user’s various privacy protection requests.

DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;however,they also cause great concern about privacy leakage.In particular,location queries can be used to infer users’sensitive private information,such as home addresses,places of work and appointment locations.Hence,many schemes providing query anonymity have been proposed,but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS.To address this challenge,a novel dual privacy-preserving scheme(DPPS)is proposed that includes two privacy protection mechanisms.First,to prevent privacy disclosure caused by correlations between locations,a correlation model is proposed based on a hidden Markov model(HMM)to simulate users’mobility and the adversary’s prediction probability.Second,to provide query probability anonymity of each single location,an advanced k-anonymity algorithm is proposed to construct cloaking regions,in which realistic and indistinguishable dummy locations are generated.To validate the effectiveness and efficiency of DPPS,theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft,i.e.,GeoLife dataset.

Privacy-Preserving Data Publishing for Multiple Numerical Sensitive Attributes

Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive- attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication, in this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization （MSB） to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.

Providing Location-Aware Location Privacy Protection for Mobile Location-Based Services

Location privacy has been a serious concern for mobile users who use location-based services provided by third-party providers via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may be not true in real-life scenarios. In this paper, we introduce a new location privacy problem： Location-aware Location Privacy Protection （L2P2） problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through extensive simulations over large synthetic and real-life datasets, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.

Privacy-Preserving Algorithms for Multiple Sensitive Attributes Satisfying t-Closeness

Although k-anonymity is a good way of publishing microdata for research purposes, it cannot resist severalcommon attacks, such as attribute disclosure and the similarity attack. To resist these attacks, many refinements of k-anonymity have been proposed with t-closeness being one of the strictest privacy models. While most existing t-closenessmodels address the case in which the original data have only one single sensitive attribute, data with multiple sensitiveattributes are more common in practice. In this paper, we cover this gap with two proposed algorithms for multiple sensitiveattributes and make the published data satisfy t-closeness. Based on the observation that the values of the sensitive attributesin any equivalence class must be as spread as possible over the entire data to make the published data satisfy t-closeness,both of the algorithms use different methods to partition records into groups in terms of sensitive attributes. One uses aclustering method, while the other leverages the principal component analysis. Then, according to the similarity of quasi-identifier attributes, records are selected from different groups to construct an equivalence class, which will reduce the lossof information as much as possible during anonymization. Our proposed algorithms are evaluated using a real dataset. Theresults show that the average speed of the first proposed algorithm is slower than that of the second proposed algorithm butthe former can preserve more original information. In addition, compared with related approaches, both proposed algorithmscan achieve stronger protection of privacy and reduce less.