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

Supporting Concurrency and Multiple Indexes in Private Access to Outsourced Data

Data outsourcing has recently emerged as a successful solution allowing individuals and organizations to delegate data and service management to external third parties. A major challenge in the data outsourcing scenario is how to guarantee proper privacy protection against the external server. Recent promising approaches rely on the organization of data in indexing structures that use encryption and the dynamic allocation of encrypted data to physical blocks for destroying the otherwise static relationship between data and the blocks in which they are stored. However, dynamic data allocation implies the need to re-write blocks at every read access, thus requesting exclusive locks that can affect concurrency. Also, these solutions only support search conditions on the values of the attribute used for building the indexing structure. In this paper, we present an approach that overcomes such limitations by extending the recently proposed shuffle index structure with support for concurrency and multiple indexes. Support for concurrency relies on the use of several differential versions of the data index that are periodically reconciled and applied to the main data structure. Support for multiple indexes relies on the definition of secondary shuffle indexes that are then combined with the primary index in a single data structure whose content and allocation is unintelligible to the server. We show how using such differential versions and combined index structure guarantees privacy, provides support for concurrent accesses and multiple search conditions, and considerably increases the performance of the system and the applicability of the proposed solution

Privacy of Outsourced Data

Abstract. Data outsourced to an external storage server are usually encrypted since there is the common assumption that all data are equally sensitive. The encrypted data however cannot be efficiently queried and their selective release is not possible or require the application of specific solutions. To overcome these problems, new proposals have been recently developed, which are based on a fragmentation technique possibly combined with encryption. The main advantage of these proposals is that they limit the use of encryption, thus improving query execution efficiency. In this paper, we describe such fragmentation-based approaches focusing in particular on the different data fragmentation models proposed in the literature. We then conclude the paper with a discussion on some research directions

Data Leak Detection As a Service: Challenges and Solutions

We describe a network-based data-leak detection (DLD) technique, the main feature of which is that the detection does not require the data owner to reveal the content of the sensitive data. Instead, only a small amount of specialized digests are needed. Our technique – referred to as the fuzzy fingerprint – can be used to detect accidental data leaks due to human errors or application flaws. The privacy-preserving feature of our algorithms minimizes the exposure of sensitive data and enables the data owner to safely delegate the detection to others.We describe how cloud providers can offer their customers data-leak detection as an add-on service with strong privacy guarantees. We perform extensive experimental evaluation on the privacy, efficiency, accuracy and noise tolerance of our techniques. Our evaluation results under various data-leak scenarios and setups show that our method can support accurate detection with very small number of false alarms, even when the presentation of the data has been transformed. It also indicates that the detection accuracy does not degrade when partial digests are used. We further provide a quantifiable method to measure the privacy guarantee offered by our fuzzy fingerprint framework

A DISTRIBUTED APPROACH TO PRIVACY ON THE CLOUD

The increasing adoption of Cloud-based data processing and storage poses a number of privacy issues. Users wish to preserve full control over their sensitive data and cannot accept it to be fully accessible to an external storage provider. Previous research in this area was mostly addressed at techniques to protect data stored on untrusted database servers; however, I argue that the Cloud architecture presents a number of specific problems and issues. This dissertation contains a detailed analysis of open issues. To handle them, I present a novel approach where confidential data is stored in a highly distributed partitioned database, partly located on the Cloud and partly on the clients. In my approach, data can be either private or shared; the latter is shared in a secure manner by means of simple grant-and-revoke permissions. I have developed a proof-of-concept implementation using an in\u2011memory RDBMS with row-level data encryption in order to achieve fine-grained data access control. This type of approach is rarely adopted in conventional outsourced RDBMSs because it requires several complex steps. Benchmarks of my proof-of-concept implementation show that my approach overcomes most of the problems

Tunable Security for Deployable Data Outsourcing

Security mechanisms like encryption negatively affect other software quality characteristics like efficiency. To cope with such trade-offs, it is preferable to build approaches that allow to tune the trade-offs after the implementation and design phase. This book introduces a methodology that can be used to build such tunable approaches. The book shows how the proposed methodology can be applied in the domains of database outsourcing, identity management, and credential management

Toward Full Accounting for Leakage Exploitation and Mitigation in Dynamic Encrypted Databases

Encrypted database draws much attention as it provides privacy-protection services for sensitive data outsourced to a third party. Recent studies show that the security guarantee of encrypted databases are challenged by several leakage-abuse attacks on its search module, and corresponding countermeasures are also proposed. Most of these studies focus on static databases, yet the case for dynamic has not been well investigated. To fill this gap, in this paper, we focus on exploring privacy risks in dynamic encrypted databases and devising effective mitigation techniques. To begin with, we systematically study the exploitable information disclosed during the database querying process, and consider two types of attacks that can recover encrypted queries. The first active attack works by injecting encoded files and correlating file volume information. The second passive attack works by identifying queries’ unique relational characteristics across updates, assuming certain background knowledge of plaintext databases. To mitigate these attacks, we propose a two-layer encrypted database hardening approach, which obfuscates both search indexes and files in a continuous way. As a result, the unique characteristics emerging after data updates can be eliminated constantly. We conduct a series of experiments to confirm the severity of our attacks and the effectiveness of our countermeasures