An effective deferentially private data releasing algorithm for decision tree

Differential privacy is a strong definition for protecting individual privacy in data releasing and mining. However, it is a rigid definition introducing a large amount of noise to the original dataset, which significantly decreases the quality of data mining results. Recently, how to design a suitable data releasing algorithm for data mining purpose is a hot research area. In this paper, we propose a differential private data releasing algorithm for decision tree construction. The proposed algorithm provides a non-interactive data releasing method through which miner can obtain the complete dataset for data mining purpose. With a given privacy budget, the proposed algorithm generalizes the original dataset, and then specializes it in a differential privacy constrain to construct decision trees. As the designed novel scheme selection operation can fully utilize the allocated privacy budget, the data set released by the proposed algorithm can yield better decision tree models than other method. Experimental results demonstrate that the proposed algorithm outperforms existing methods for private decision tree construction.

Routing and privacy protection in human associated delay tolerant networks

This thesis proposes Human Associated Delay Tolerant Networks, where data communications among mobile nodes are determined by human social behaviours. Three models are proposed to handle the social attributes effect on data forwarding, the time impact on nodes’ movement and the privacy protection issue when social attributes are introduced.

Gaming privacy : a Canadian case study of a children’s co-created privacy literacy game

This paper reports the process and outcomes of the design of a game that educates children about management of privacy online. Using a participatory action research process, children worked with the researchers to develop and play a game which simulates certain aspects of online privacy management and allows for scaffolded experiential learning in a safe environment. The game allows children to develop autonomous skills and understandings, not only for more effective learning but also because it is only through autonomy that children can develop a sense of self which is necessary for understanding what it means to be private. The paper shows that children have quite sophisticated understandings of privacy, compared with some adult perceptions, and that these understandings include awareness of the risks posed by commercial organisations seeking to gather personal data from them. The paper shows how engaging children as research and design participants can lead to more successful approaches in the development of privacy literacy.

Real and substantial connections : Enforcing Canadian privacy laws against American social networking companies

Any organisation that captures personal data in Canada for processing is deemed tohave a ‘real and substantial connection’ to Canada and thus fall within thejurisdiction of the Personal Information Protection and Electronic Documents Act(PIPEDA) and of the Office of the Privacy Commissioner of Canada (OPC). Whathas been the experience of enforcing Canadian privacy protection law on US-basedsocial networking services? We analyse some of the high-profile enforcement actionsby the Privacy Commissioner. We also test compliance through an analysis of theprivacy policies of the top 23 SNSs operating in Canada and through the use of accessto personal information requests. Our analysis suggests that non-compliance iswidespread, and is explained by the countervailing conceptions of jurisdictioninherent in corporate policy and technical system design.

Chameleon hashing for secure and privacy-preserving vehicular communications

Many services and applications in vehicular ad-hoc networks (VANETs) require preserving and secure data communications. To improve driving safety and comfort, the traffic-related status information will be broadcasted regularly and shared among drivers. Without the security and privacy guarantees, attackers could track their interested vehicles by collecting and analyzing their traffic messages. Hence, anonymous message authentication is an essential requirement of VANETs. On the other hand, when a vehicle is involved in a dispute event of warning message, the certificate authority should be able to recover the real identity of this vehicle. To deal with this issue, we propose a new privacy-preserving authentication protocol with authority traceability using elliptic curve based chameleon hashing. Compared with existing schemes, our approach possesses the following features: 1) mutual and anonymous authentication for both vehicle-to-vehicle and vehicle-to-roadside communications, 2) vehicle unlinkability, 3) authority tracking capability, and 4) high computational efficiency. We also demonstrate the merits of our proposed scheme through security analysis and extensive performance evaluation.

A survey on differential privacy and applications

Privacy preserving in data release and mining is a hot topic in the information security field currently. As a new privacy notion, differential privacy (DP) has grown in popularity recently due to its rigid and provable privacy guarantee. After analyzing the advantage of differential privacy model relative to the traditional ones, this paper surveys the theory of differential privacy and its application on two aspects, privacy preserving data release (PPDR) and privacy preserving data mining (PPDM). In PPDR, we introduce the DP-based data release methodologies in interactive/non-interactive settings and compare them in terms of accuracy and sample complexity. In PPDM, we mainly summarize the implementation of DP in various data mining algorithms with interface-based/fully access-based modes as well as evaluating the performance of the algorithms. We finally review other applications of DP in various fields and discuss the future research directions.

A privacy-preserving location tracking system for smartphones based on cloud storage

With the widespread use of smartphones, the loss of a device is a critical problem, which results both in disrupting daily communications and losing valuable property. As a result, tracking systems have been developed to track mobile devices. Previous tracking systems focus on recovering the device's locations after it goes missing, with security methods implemented on the clients. However, users' locations are stored in untrusted third-party services, which may be attacked or eavesdropped. In this paper, we propose a system, named Android Cloud Tracker, to provide a privacy-preserving tracking client and safe storing of user's locations. We use cloud storage controlled by users themselves as storage facilities, and they do not need to worry about any untrusted third party. We implement Android Cloud Tracker prototype on Android phones, and the evaluation shows that it is both practical and lightweight: it generates a small amount of data flow and its distributed architecture provides strong guarantees of location privacy while preserving the ability to efficiently track missing devices.

A differentially private algorithm for location data release

The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations: private location clustering shrinks the randomized domain and cluster weight perturbation hides the weights of locations, while private location selection hides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets, GeoLife, Flickr, Div400 and Instagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

Cost-effective authentic and anonymous data sharing with forward security

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Security and privacy concerns for Australian SMEs cloud adoption: empirical study of metropolitan vs regional SMEs

New national infrastructure initiatives such as the National Broadband Network (NBN) allow small and medium-sized enterprises (SMEs) in Australia to have greater access to cost effective Cloud computing. However, the ability of Cloud computing to store data remotely and share services in a dynamic environment brings with it security and privacy concerns. Evaluating these concerns is critical to address the Cloud computing underutilisation issue and leverage the benefits of costly NBN investment. This paper examines the influence of privacy and security factors on Cloud adoption by Australian SMEs in metropolitan and regional area. Data were collected from 150 Australian SMEs (specifically, 79 metropolitan SMEs and 71 regional SMEs) and structural equation modelling was used for the analysis. The findings reveal that privacy and security factors do not significantly influence the decision-making of Australian SMEs in the adoption of Cloud computing. Moreover, the results indicate that Cloud computing adoption is not influenced by the geographical location (i.e., metropolitan or regional location) of the SMEs. The findings extend the current understanding of Cloud computing adoption by Australian SMEs. The results will be useful to SMEs, Cloud service providers and policy makers devising Cloud security and privacy policies.

Privacy aware K-means clustering with high utility

Privacy-preserving data mining aims to keep data safe, yet useful. But algorithms providing strong guarantees often end up with low utility. We propose a novel privacy preserving framework that thwarts an adversary from inferring an unknown data point by ensuring that the estimation error is almost invariant to the inclusion/exclusion of the data point. By focusing directly on the estimation error of the data point, our framework is able to significantly lower the perturbation required. We use this framework to propose a new privacy aware K-means clustering algorithm. Using both synthetic and real datasets, we demonstrate that the utility of this algorithm is almost equal to that of the unperturbed K-means, and at strict privacy levels, almost twice as good as compared to the differential privacy counterpart.

Steno cipher to provide data confidentiality and tampered data recovery for RFID tag

Radio Frequency Identification (RFID) is an emerging wireless object identification technology with many potential applications such as supply chain management, personnel tracking and healthcare. However, security vulnerabilities of the RFID system have been a serious concern for its wide adoption in many applications. Although much work has been done to provide privacy and anonymity, little focus has been given to ensure RFID data confidentiality, integrity and to address the tampered data recovery problem. To this end, we propose a lightweight stenographic-based approach to ensure RFID data confidentiality and integrity as well as the recovery of tampered RFID data. © 2013 Springer-Verlag Berlin Heidelberg.

Time-series pattern based effective noise generation for privacy protection on cloud

Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer's private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests' occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer's privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk.

Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.