Location Data Anonymization Retaining Data Mining Utilization

In the era of big data, privacy preservation has been the focus for data mining. Mondrian anonymization is a state-of-the-art data anonymization algorithm for relational dataset, widely used in many classical syntactic privacy-preserving data mining methods, like k-anonymity, l-diversity, t-closeness, etc. Mondrian anonymization is named for its multidimensional data partitioning in geometric space to find the best partitions for data anonymization. However, one problem with using Mondrian anonymization is taking too much time and memory for the high-dimensional data. Another problem is that the Mondrian-based privacy preservation may lead to the unstable performance of data mining models. For example, in Mondrian-based k-anonymity, the accuracy results of data mining may drop dramatically with the growth of k value. For solving these problems, in this paper we propose an enhanced Mondrian anonymization model based on Self-Organizing Map (SOM-Mondrian). With the help of SOM, multidimensional data are converted from a high dimensional space into two-dimensional space; at the same time, preserving their topological properties of the input space. The resulting two-dimensional data are then used by Mondrian algorithm to find the best partitions for data anonymization. To our best knowledge, we are the first to propose SOM-based method for Mondrian anonymization. Experimental results show that, after applying our proposed method, the processing time of Mondrian anonymization decreases significantly from 12.11 seconds to 0.16 seconds; besides, the accuracy of data mining applications increases, about 2% higher than the results under the standard Mondrian anonymization, and also shows steadier and more robust (the degree of variation is reduced by 75%) to the varying k value.

Hap-Cp-Abe Based Encryption Technique With Hashed Access Policy Based Authentication Scheme For Privacy Preserving Of Phr

With the swift proliferation of Internet services and always connected smart devices, users continue to (un)intentionally share copious amount of data on daily basis. While the availability of such a big amount of data is useful to extract interesting nuggets in areas such as behavioral or medical research, it also brings about unprecedented user information privacy violation consequences, e.g., identity theft and reputation damage of target users. This is aggravated when users share Privacy Sensitive Information (PSI) on-line, often times, including to unintended audience. In this regard, detecting PSI disclosure becomes an essential step towards tackling the long(short) term privacy consequences of divulging such information. As such, in this paper, we present PrivacyBot, a machine-learning based proof-of-concept that detects PSI in user-generated unstructured texts. A rigorous set of experiments show that our approach can detect PSI with an accuracy of up-to 95%. Furthermore, PrivacyBot provides a fine-grained category of PSI types (with an accuracy of up-to 88%), defined based on existing work and Art. §9 of the European Union (EU) General Data Protection Regulation (GDPR). Results are promising and shed light on the possibility of integrating such tools to support users in making informed privacy related decisions when disclose PSI on-line.

The availability of textual data depicting human-centered features and behaviors is crucial for many data mining and machine learning tasks. However, data containing personal information should be anonymized prior making them available for secondary use. A variety of text anonymization methods have been proposed in the last years, which are standardly evaluated by comparing their outputs with human-based anonymizations. The residual disclosure risk is estimated with the recall metric, which quantifies the proportion of manually annotated re-identifying terms successfully detected by the anonymization algorithm. Nevertheless, recall is not a risk metric, which leads to several drawbacks. First, it requires a unique ground truth, and this does not hold for text anonymization, where several masking choices could be equally valid to prevent re-identification. Second, it relies on human judgements, which are inherently subjective and prone to errors. Finally, the recall metric weights terms uniformly, thereby ignoring the fact that the influence on the disclosure risk of some missed terms may be much larger than of others. To overcome these drawbacks, in this paper we propose a novel method to evaluate the disclosure risk of anonymized texts by means of an automated re-identification attack. We formalize the attack as a multi-class classification task and leverage state-of-the-art neural language models to aggregate the data sources that attackers may use to build the classifier. We illustrate the effectiveness of our method by assessing the disclosure risk of several methods for text anonymization under different attack configurations. Empirical results show substantial privacy risks for most existing anonymization methods.

The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data.In existing approaches the outlier detection done only on numeric dataset.For outlier detection if we use clustering method , then they mainly focus on those elements as outliers which are lying outside the clusters but it may possible that some of the unknown elements with any possible reasons became the part of the cluster so we have to concentrate on that also.The Proposed method uses hybrid approach to reduce the number of outliers.The number of outlier can only reduce by improving the cluster formulation method.The proposed method uses two data mining techniques for cluster formulation i.e. weighted k-means and neural network where weighted kmeans is the clustering technique that can apply on text and date data set as well as numeric data set.Weighted kmeans assign the weights to each element in dataset.The output of weighted k-means becomes the input for neural network where the neural network is the classification and clustering technique of data mining.Training is provided to the neural network and according to that neurons performed the testing.The neural network test the cluster formulated by weighted k-means to ensure that the clusters formulated by weighted k-means are group accordingly.There is lots of outlier detection methods present in data mining.The proposed method use Integrating Semantic Knowledge (SOF) for outlier detection.This method detects the semantic outlier where the semantic outlier is a data point that behaves differently with other data points in the same class or cluster.The main motive of this research work is to reduce the number of outliers by improving the cluster formulation methods so that outlier rate reduces and also to decrease the mean square error and improve the accuracy.The simulation result clearly shows that proposed method works pretty well as it significantly reduces the outlier.

A federated learning method for real-time emotion state classification from multi-modal streaming.

Imagine receiving an email from cybercriminals stating that all your personal information has been compromised— name, date of birth, home address, and finances. They are demanding money from you in exchange for not leaking your sensitive information. It's a terrifying situation to be in, isn't it? We adopt an NIDS-based approach for web application penetration testing in order to resolve this issue. Web application penetration testing is an ongoing security evaluation that mimics actual attacks to evaluate how secure web applications are. The main objective is to find any possible flaws, configuration errors, or vulnerabilities that malicious users can use to jeopardize a web application's availability, confidentiality, or integrity. An NIDS is deployed to detect fraudulent activities at the network level, which can complement conventional penetration testing techniques that concentrate on flaws in the software. The goal of this research is to improve the identification of security vulnerabilities at the network and application levels by combining traditional web application penetration testing with Network Intrusion Detection Systems (NIDS).

Medical data mining and knowledge discovery can benefit from the experience and knowledge of clinicians, however, the implementation of this data mining system is challenging. Unlike traditional data mining methods, in this class of applications we process data with some posterior knowledge and the target function is more complex and even may include the opinion of user. Despite the success of the classical reasoning algorithms in many common data mining applications, they failed to address medical record processing where we need to extract information from incomplete, small samples along with an external rulebase to generate ‘meaningful’ interpretation of biological phenomenon. Swarm intelligence is an alternative class of flexible approaches that is promising in data mining. With full control over the rule extraction target function, particle swarm optimization (PSO) is a suitable approach for data mining subject to a rulebase which defines the quality of rules and constancy with previous observations. In this chapter we describe a complex clinical problem that has been addressed using PSO data mining. A large group of temporal lobe epilepsy patients are studied to find the best surgery candidates. Since there are many parameters involved in the decision process, the problem is not tractable from traditional data mining point of view, while the new approach that uses the field knowledge could extract valuable information. The proposed method allows expert to interaction with data mining process by offering manual manipulation of generated rules. The algorithm adjusts the rule set with regard to manipulations. Each rule has a reasoning which is based on the provided rulebase and similar observed cases. Support vector machine (SVM) classifier and swarm data miner are integrated to handle joint processing of raw data and rules. This approach is used to establish the limits of observations and build decision boundaries based on these critical observations.

Conventional text embeddings, typically learned in the Euclidean space, may struggle to effectively capture word semantics based solely on the directional similarity between word vectors. In contrast, spherical text embeddings have demonstrated remarkable efficacy in various natural language processing (NLP) tasks recently. However, training spherical text generative models (STGMs) requires large representative datasets, which could potentially contain sensitive private information. To mitigate this concern, we propose a novel approach: the differential private spherical text generative model (DP-STGM), which facilitates learning text embeddings within the spherical space while ensuring privacy via efficient Riemannian optimization and the framework of differential privacy. To evaluate the efficacy of our privacy-preserving algorithm, we initially train an adversary using an external dataset without the application of differential privacy. Subsequently, we introduce two metrics to measure the model’s ability to protect privacy: (1) cosine similarity between recovered words from the adversary that generates the spherical text embeddings and those generated by DP-STGM, and (2) Top-n rank correlation. Our experimental findings demonstrate that DP-STGM outperforms baseline models, showcasing its superior performance. By leveraging the power of differential privacy in the Riemannian optimization process, our model achieves better preservation of sensitive information while simultaneously capturing the semantic nuances inherent in word embeddings. As a result, DP-STGM represents a robust and efficient solution for NLP tasks that require privacy protection without compromising on the quality of learned text embeddings. By offering a privacy-preserving alternative, DP-STGM broadens the range of applications for which STGMs can be safely employed, ensuring data privacy while harnessing the rich information contained in spherical text embeddings. Our work opens up new avenues for future research in privacy-aware NLP and advances the state-of-the-art in both privacy protection and semantic learning in this domain.

Recently, data mining over transactional data streams has become an attractive research area. However, releasing raw transactional data streams, in which only explicit identifying information must be removed, may compromise individual privacy. Many privacy-preserving approaches have been proposed for publishing static transactional data. Due to the characteristics of data streams, which must be processed quickly, static data anonymization methods cannot be directly applied to data streams. In this paper, we first analyze the privacy problem in publishing transactional data streams based on a sliding window. Then, we present two dynamic algorithms with generalization and suppression to anonymize continuously a sliding window to make it satisfy $\rho $ -uncertainty by structuring an affected sensitive rules trie, because the removal and addition of transactions may make the current sliding window fail to satisfy $\rho $ -uncertainty. Experimental results show that our methods are more efficient than sliding window anonymization with batch processing by using existing static anonymization methods.

As a result of the huge amounts of data available in any subject of research, data mining technologies confront considerable obstacles. Due to the large volume of data, most existing data mining methods are inapplicable to many real-world problems. Data mining methods become ineffective when the problem size becomes too large. Scalability is an issue that must be addressed in data mining algorithms in order to construct high-performance, efficient, and scalable data mining algorithms. A new scalability technique is proposed and applied to several data mining problems in this research. The proposed scaling methodology is developed using a cascade approach. The approach begins with the collection of a large data set from several sources, which is then preprocessed. Once the dataset has been preprocessed, decompose it into smaller data sets of equal size subsets. Then apply a data mining approach to each subset, with the identical data mining method stated for each subset. The findings of the data mining approach on all subsets are pooled and aggregated for the final output. The performance of the suggested algorithm is assessed using a number of criteria, including accuracy, precision, recall, F-score, and execution time. Social advertising and bank marketing are the two datasets on which the proposed method is tested. The suggested approach's performance is compared with non-scale data mining methods, and it is found that the scaling method outperformed non-scale data mining methods on all measures.

Today, security is a requirement for smartphone operating systems that are used to store and handle sensitive information. However, smartphone users usually download third-party applications that can leak personal data without user authorization. For this reason, the dynamic taint analysis mechanism is used to control the manipulation of private data by third-party apps [9]. But this technique does not detect control flows. In particular, untrusted applications can circumvent Android system and get privacy sensitive information through control flows. In this paper, we propose a hybrid approach that combines static and dynamic analysis to propagate taint along control dependencies in Android system. To evaluate the effectiveness of our approach, we analyse 27 free Android applications. We found that 14 of these applications use control flows to transfer sensitive data. We successfully detect that 8 of them leaked private information. Our approach creates 19 % performance overhead that is due to the propagation of taint in the control flow. By using our approach, it becomes possible to detect leakage of personal data through control flows.

As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and security of sensitive information within these data streams is a fundamental concern. We explore cryptographic techniques, anonymization methods, and access control mechanisms that safeguard data while allowing for meaningful analysis. We present novel adaptive algorithms and model update strategies that can continuously learn and adjust to changing data distributions. These approaches enable data stream mining to remain effective and accurate over time. This paper offers insights into the fusion of security and adaptability in data stream mining, providing a foundation for the development of robust and privacy-conscious solutions for the evolving landscape of new-generation big data applications.

Privacy preserving rule-based classifier using modified artificial bee colony algorithm

This article describes how taxi service is an essential means of mobility in many cities. Recent findings show that average automobile owners utilize their vehicles for only 5% of its time in a day. Therefore, the advent of autonomous vehicles and car sharing will make it possible for owners to engage their vehicles as taxis when not in use by utilizing its 95% free time for income generation. Sensitive private information is required to be released during a taxi service delivery, which may bring certain security and privacy issues and challenges. This may hinder the prospect of using autonomous vehicles as a form of taxi. As a result of these, the authors propose a secure and privacy-preserving taxi service framework for car sharing, which ensures protection of car owner and passengers personal details, e.g. identity, location, destination, etc. The authors developed a decay-based trust model for a framework in order to monitor and improve the quality of service rendered to passengers by vehicles. The decay-based trust model was simulated on the framework. The simulation of the decay-based trust model shows that it is a perfect model for rewarding vehicles which render good quality of service and blacklisting vehicles with frequent poor service delivery.