A secure data outsourcing scheme based on Asmuth – Bloom secret sharing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing

Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners

The k-nearest neighbors (k-NN) algorithm is a popular and effective classification algorithm. Due to its large storage and computational requirements, it is suitable for cloud outsourcing. However, k-NN is often run on sensitive data such as medical records, user images, or personal information. It is important to protect the privacy of data in an outsourced k-NN system. Prior works have all assumed the data owners (who submit data to the outsourced k-NN system) are a single trusted party. However, we observe that in many practical scenarios, there may be multiple mutually distrusting data owners. In this work, we present the first framing and exploration of privacy preservation in an outsourced k-NN system with multiple data owners. We consider the various threat models introduced by this modification. We discover that under a particularly practical threat model that covers numerous scenarios, there exists a set of adaptive attacks that breach the data privacy of any exact k-NN system. The vulnerability is a result of the mathematical properties of k-NN and its output. Thus, we propose a privacy-preserving alternative system supporting kernel density estimation using a Gaussian kernel, a classification algorithm from the same family as k-NN. In many applications, this similar algorithm serves as a good substitute for k-NN. We additionally investigate solutions for other threat models, often through extensions on prior single data owner systems

Preserving Both Privacy and Utility in Network Trace Anonymization

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We present the general approach and instantiate it based on CryptoPAn. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The results show that our approach can significantly reduce the level of information leakage (e.g., less than 1\% of the information leaked by CryptoPAn) with comparable utility

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

A Privacy-Preserving Outsourced Data Model in Cloud Environment

Nowadays, more and more machine learning applications, such as medical diagnosis, online fraud detection, email spam filtering, etc., services are provided by cloud computing. The cloud service provider collects the data from the various owners to train or classify the machine learning system in the cloud environment. However, multiple data owners may not entirely rely on the cloud platform that a third party engages. Therefore, data security and privacy problems are among the critical hindrances to using machine learning tools, particularly with multiple data owners. In addition, unauthorized entities can detect the statistical input data and infer the machine learning model parameters. Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency. In order to protect the data of data owners, the epsilon-differential privacy is used, and fog nodes are used to address the problem of the lower bandwidth and latency in this proposed scheme. The noise is produced by the epsilon-differential mechanism, which is then added to the data. Moreover, the noise is injected at the data owner site to protect the owners data. Fog nodes collect the noise-added data from the data owners, then shift it to the cloud platform for storage, computation, and performing the classification tasks purposes

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward