Privacy-preserving Approach Research Articles

An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks

With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated by the applications of LBS, but users’ privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users’ privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.

Privacy-Preserved Data Sharing for Evidence-Based Policy Decisions: A Demonstration Project Using Human Services Administrative Records for Evidence-Building Activities

Privacy-Preserved Data Sharing for Evidence-Based Policy Decisions: A Demonstration Project Using Human Services Administrative Records for Evidence-Building Activities

An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack

As the amount of data and computational power explosively increase, valuable results are being created using machine learning techniques. In particular, models based on deep neural networks have shown remarkable performance in various domains. On the other hand, together with the development of neural network models, privacy concerns have been raised. Recently, as privacy breach attacks on training datasets of neural network models have been proposed, research on privacy-preserving neural networks have been conducted. Among the privacy-preserving approaches, differential privacy provides a strict privacy guarantee, and various differentially private mechanisms have been studied for neural network models. However, it is not clear how appropriate privacy parameters should be chosen, considering the model’s performance and the degree of privacy guarantee. In this paper, we study how to set appropriate privacy parameters to preserve differential privacy based on the resistance to privacy breach attacks in neural networks. In particular, we focus on the model inversion attack for neural network models, and study how to apply differential privacy as a countermeasure against this attack while retaining the utility of the model. In order to quantify the resistance to the model inversion attack, we introduce a new attack performance metric, instead of a survey-based approach, by leveraging a deep learning model, and capture the relationship between attack probability and the degree of privacy guarantee.

A survey on game theoretic approaches for privacy preservation in data mining and network security

Game theory has found widespread use in various fields like Economics, Biology, Political Science, etc. and forms an aegis for logical decision making in these areas. In computer science, due to advancing technologies, there has been a pressing need to use game theory in various problems due to the lack of scalability of traditional solutions. There has been ongoing research in various fields of computer science like security, machine learning, cloud computing, etc. where game theoretic approaches are extensively used. In this paper, we present a review on game theoretical approaches to various fields in computer science such as privacy preservation, network security and intrusion detection and resource optimization. In the end, this paper provides a comparative study of various game models used in different applications in a tabular format.

The purpose driven privacy preservation for accelerometer-based activity recognition

Accelerometer-based activity recognition (AAR) attracted a lot of attentions due to the wide spread of smartphones with energy-efficiency. However, since accelerometer data contains individual characteristics; AAR might raise privacy concerns. Although numerous privacy preservation approaches, such as ”privacy filtering, differential privacy, and inferential privacy”, have been proposed to conceal sensitive information, unfortunately they cannot address the privacy problem associated with AAR. In this paper, we report our efforts to control the use of the AAR while preserving the privacy. To achieve this task, our method leverages a connection to agglomerative information bottleneck, through which the amount of disclosed data can be compressed so that irrelevant private information can be reduced, and a connection to general privacy statistical inference framework, where both of the privacy leakage and utility accuracy are considered as mutual information. Our experimental results have shown that the proposed solution can greatly reduce privacy leakage while maintaining a relative good utility.

A Novel Approach for Privacy Preservation in Bigdata Using Data Perturbation in Nested Clustering in Apache Spark

A Novel Approach for Privacy Preservation in Bigdata Using Data Perturbation in Nested Clustering in Apache Spark

A Multi-Level Privacy-Preserving Approach to Hierarchical Data Based on Fuzzy Set Theory

Nowadays, more and more applications are dependent on storage and management of semi-structured information. For scientific research and knowledge-based decision-making, such data often needs to be published, e.g., medical data is released to implement a computer-assisted clinical decision support system. Since this data contains individuals’ privacy, they must be appropriately anonymized before to be released. However, the existing anonymization method based on l-diversity for hierarchical data may cause serious similarity attacks, and cannot protect data privacy very well. In this paper, we utilize fuzzy sets to divide levels for sensitive numerical and categorical attribute values uniformly (a categorical attribute value can be converted into a numerical attribute value according to its frequency of occurrences), and then transform the value levels to sensitivity levels. The privacy model ( α l e v h , k)-anonymity for hierarchical data with multi-level sensitivity is proposed. Furthermore, we design a privacy-preserving approach to achieve this privacy model. Experiment results demonstrate that our approach is obviously superior to existing anonymous approach in hierarchical data in terms of utility and security.

A Privacy Preserving Approach to Collaborative Systemic Risk Identification: The Use-Case of Supply Chain Networks

Globalization and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network, it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of network’s structure can endanger the whole supply chain network’s robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms’ competitive advantage.

A fog-based privacy-preserving approach for distributed signature-based intrusion detection

Intrusion detection systems (IDSs) are the frontier of defense against transmissible cyber threats that spread across distributed systems. Modern IDSs overcome the limitation of hardware processing power by offloading computation extensive operations such as signature matching to the cloud. Moreover, in order to prevent the rapid spread of transmissible cyber threats, collaborative intrusion detection schemes are widely deployed to allow distributed IDS nodes to exchange information with each other. However, no party wants to disclose their own data during the detection process, especially sensitive user data to others, even the cloud providers for privacy concerns. In this background, privacy-preserving technology has been researched in the field of intrusion detection, whereas a collaborative intrusion detection network (CIDN) environment still lacks of appropriate solutions due to its geographical distribution. With the advent of fog computing, in this paper, we propose a privacy-preserving framework for signature-based intrusion detection in a distributed network based on fog devices. The results in both simulated and real environments demonstrate that our proposed framework can help reserve the privacy of shared data, reduce the workload on the cloud side, and offer less detection delay as compared to similar approaches.

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

Two privacy-preserving approaches for data publishing with identity reservation

Many approaches have been proposed for publishing useful information while preserving data privacy. Among them, the privacy models of identity-reserved (k, l)-anonymity and identity-reserved (alpha , beta )-anonymity have been proposed to handle the situation where an individual could have multiple records. However, the two models fail to prevent attribute disclosure. To this end, we propose two new privacy models: enhanced identity-reserved l-diversity and enhanced identity-reserved (alpha , beta )-anonymity. Moreover, to implement the two privacy models we design a general anonymization algorithm, called DAnonyIR, with clustering technique by calling different decision functions, which can decrease the information loss caused by generalization. Further, we compare DAnonyIR concerning our two privacy models with existing generalization method GeneIR concerning identity-reserved (k, l)-anonymity and identity-reserved (alpha , beta )-anonymity, respectively. The experimental results show that our two approaches provide stronger privacy preservation, and their information loss and relative error ratio of query answering are less than those of GeneIR.

Privacy-Preserving Policy Iteration for Decentralized POMDPs

We propose the first privacy-preserving approach to address the privacy issues that arise in multi-agent planning problems modeled as a Dec-POMDP. Our solution is a distributed message-passing algorithm based on trials, where the agents' policies are optimized using the cross-entropy method. In our algorithm, the agents' private information is protected using a public-key homomorphic cryptosystem. We prove the correctness of our algorithm and analyze its complexity in terms of message passing and encryption/decryption operations. Furthermore, we analyze several privacy aspects of our algorithm and show that it can preserve the agent privacy of non-neighbors, model privacy, and decision privacy. Our experimental results on several common Dec-POMDP benchmark problems confirm the effectiveness of our approach.

Progressive Scrambling for Social Media

Despite research work achieving progress in preserving the privacy of user profiles and visual surveillance, correcting problems in social media have not taken a great step. The reason is the lack of effective modelling, computational algorithms, and resultant evaluations in quantitative research. In this article, the authors take social media into consideration and link users together under the umbrella of social networks so as to exploit a way that the potential problems related to media privacy could be solved. The author's contributions are to propose tensor product-based progressive scrambling approaches for privacy preservation of social media and apply our approaches to the given social media which may encapsulate privacy before being viewed so as to achieve the goal of privacy preservation in anonymity, diverse and closeness. These approaches fully preserve the media information of the scrambled image and make sure it is able to be restored. The results show the proposed privacy persevering approaches are effective and have outstanding performance in media privacy preservation.

ILLIA: Enabling <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Anonymity-Based Privacy Preserving Against Location Injection Attacks in Continuous LBS Queries

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location ${k}$ -anonymity, strives to aggregate the queries of ${k}$ nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than ${1/k}$ , thus offering privacy preservation for users. One inherent limitation of ${k}$ -anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one’s location privacy could be greatly more than ${1/k}$ , yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables ${k}$ -anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based ${k}$ -anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

Two Privacy-Preserving Approaches for Publishing Transactional Data Streams

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.

VANET Security and Privacy - An Overview

Even though vehicular ad-hoc networks (VANETs) bring tremendous benefits to society, yet they raise many challenges where the security and privacy concerns are the most critical ones. In this paper, we provide a detailed overview of the state-of-the-art security and privacy requirements in VANET. Also, a brief of the approaches that are proposed in the literature to fulfill these requirements is given in this paper. Besides that, a classification of the various VANET attacks based on the communication system layers is provided in this paper. In addition, the different types of VANET adversaries and attackers are presented here. In general, this paper aims to provide a good piece of information about VANET security and privacy, in order to be used as a tool to help researchers in this field in developing secure privacy-preserving approaches for VANET.

SCRAPPOR: An Efficient Privacy-Preserving Algorithm Base on Sparse Coding for Information-Centric IoT

Different from the traditional Internet-of-Things (IoT) architecture, information-centric IoT is a novel paradigm in which changes are made to the entire network stack, from layer 3 up to the application layer. IC-IoT is built on top of named data networking (NDN), a content-centric Internet paradigm whose features are particularly promising for certain IoT applications, such as smart grid. In IC-IoT, privacy is one of the most challenging issues. Among existing privacy-preserving approaches, differential privacy (DP) is a powerful tool that can provide privacy-preserving noisy query answers over statistical databases and has been widely adopted in many practical fields. In particular, as an enhanced implementation of DP, randomized aggregable privacy-preserving ordinal response (RAPPOR) can achieve strong privacy, high-efficiency, and high-utility guarantees for each client string in data crowdsourcing. However, in many IoT applications like smart grid, data are often processed in batches. Developing a new random response algorithm that can support batch-processing will make it more efficient and suitable for IoT applications than existing random response algorithms. In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guarantees for consumer’s behaviors and can process one batch of data at each time. First, by applying sparse coding in this algorithm, a behavior signature dictionary is created from the aggregated energy consumption data in IoT. Then, we add noise into the behavior signature dictionary by the classical randomized response techniques to achieve the differential privacy after data re-aggregation. Through security analysis with the principle of differential privacy and experimental performance evaluation, we prove that our proposed algorithm can preserve consumer’s privacy without compromising utility.

Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart Grid

A key enabler for the smart grid is the fine-grained monitoring of power utilization. Although such a mechanism is helpful in the optimization of the whole electricity generation, distribution, and consumption cycle, it also creates opportunities for the potential adversaries in deducing the activities and habits of the subscribers. In fact, by utilizing the standard and readily available tools of nonintrusive load monitoring (NILM) techniques on the metered electricity data, many details of customers’ personal lives can be easily discovered. Therefore, prevention of such adversarial exploitations is of utmost importance for privacy protection. One strong privacy preservation approach is the modification of the metered data through the use of on-site storage units in conjunction with renewable energy resources. In this study, we introduce a novel mathematical programming framework to model eight privacy-enhanced power-scheduling strategies inspired and elicited from the literature. We employ all the relevant techniques for the modification of the actual electricity utilization (i.e., on-site battery, renewable energy resources, and appliance load moderation). Our evaluation framework is the first in the literature, to the best of our knowledge, for a comprehensive and fair comparison of the load-shaping techniques for privacy preservation. In addition to the privacy concerns, we consider monetary cost and disutility of the users in our objective functions. Evaluation results show that privacy preservation strategies in the literature differ significantly in terms of privacy, cost, and disutility metrics.

On location-privacy in opportunistic mobile networks, a survey

In recent years, networked devices spread into different activities in our daily lives enabling many innovative applications. These applications were not only computationally-intelligent on their own, but also enabled collection and sharing of users' information via opportunistic and pervasive communications. This omnipresence of devices capable of continuously tracking sensitive contexts and whereabouts raised users' concerns about their own privacy. In this paper, we present a survey of privacy-protection solutions proposed for Opportunistic Networks. Firstly, we provide a detailed study of the privacy problem and major privacy-related attacks. Secondly, we present contemporary research efforts aiming to protect users' privacy with special focus on location-privacy problem, while discussing the applicability of each solution in Opportunistic Networks (OppNet). Thirdly, a taxonomy of privacy-preserving approaches is proposed showing the major trends in addressing this challenging problem. A systematic evaluation of the advantages and disadvantages is presented. Using this study, we present various design space elements (criteria and factors) required to successfully offer better location-privacy. Moreover, we propose a unified framework that addresses these design space elements while enabling opportunistic communications between users. To illustrate our purpose, we apply this framework to a protocol that offers location-privacy through obfuscation in mobile opportunistic networks. This illustration shows the feasibility and effectiveness of our proposed framework. We explore key design space elements: criteria (such as scalability) and factors (such as social-awareness of ties, and location-awareness) to achieve the highest possible privacy level under different network conditions and nodes' requirements.

A Survey on Heuristic Based Approach for Privacy Preserving in Data Mining

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