Distributed shuffle index: Analysis and implementation in an industrial testbed

The protection of content confidentiality as well as of access and pattern confidentiality of data moved to the cloud have been recently the subject of several investigations. The distributed shuffle index addresses these issues by randomly partitioning data among three independent cloud providers. In this paper, we describe the implementation of the distributed shuffle index in the high-performance Dell EMC platform. We first illustrate the main characteristics of the industry testbed and then show the results of our experiments, confirming the limited performance overhead and the practical applicability of the distributed shuffle index in industrial environments

Distributed Shuffle Index in the Cloud: Implementation and Evaluation

The distributed shuffle index strengthens the guarantees of access confidentiality provided by the shuffle index through the distribution of data among three cloud providers. In this paper, we analyze architectural and design issues and describe an implementation of the distributed shuffle index integrated with different cloud providers (i.e., Amazon S3, OpenStack Swift, Google Cloud Storage, and EMC Elastic Cloud Storage). The experimental results obtained with our implementation confirm the protection guarantees provided by the distributed shuffle index and its limited performance overhead, demonstrating its practical applicability in cloud scenarios

Data Security and Privacy in the Cloud

Relying on the cloud for storing data and performing computations has become a popular solution in today\u2019s society, which demands large data collections and/or analysis over them to be readily available, for example, to make knowledge-based decisions. While bringing undeniable benefits to both data owners and end users accessing the outsourced data, moving to the cloud raises a number of issues, ranging from choosing the most suitable cloud provider for outsourcing to effectively protecting data and computation results. In this paper, we discuss the main issues related to data protection arising when data and/or computations over them are moved to the cloud. We also illustrate possible solutions and approaches for addressing such issues

Range Queries on an encrypted outsourced database

This project is about retrieving data in range without allowing the server to read it, when the database is stored in the server. Basically, our goal is to build a database that allows the client to maintain the confidentiality of the data stored, despite all the data is stored in a different location from the client's hard disk. This means that all the information written on the hard disk can be easily read by another person who can do anything with it. Given that, we need to encrypt that data from eavesdroppers or other people. This is because they could sell it or log into accounts and use them for stealing money or identities. In order to achieve this, we need to encrypt the data stored in the hard drive, so that only the possessor of the key can easily read the information stored, while all the others are going to read only encrypted data. Obviously, according to that, all the data management must be done by the client, otherwise any malicious person can easily retrieve it and use it for any malicious intention. All the methods analysed here relies on encrypting data in transit. In the end of this project we analyse 2 theoretical and practical methods for the creation of the above databases and then we tests them with 3 datasets and with 10, 100 and 1000 queries. The scope of this work is to retrieve a trend that can be useful for future works based on this project

Cloud Security: Issues and Concerns

The cloud has emerged as a successful computing paradigm, allowing users and organizations to rely on external providers to store and process their data and make it available to others. An increasingly important priority, if there is to be wide adoption and acceptance of cloud computing, is for data owners and users to have security guarantees. Guaranteeing security means ensuring confidentiality and integrity of data, access to it, and computations with it, and ensuring availability of data and services to legitimate users in compliance with agreements with the providers. In this chapter, we present an overview of the main security issues and concerns arising in the cloud scenario, in particular with respect to the storage, management, and processing of data

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

Enforcing authorizations while protecting access confidentiality

Cloud computing is the reference paradigm to provide data storage and management in a convenient and scalable manner. However, moving data to the cloud raises several issues, including the confidentiality of data and of accesses that are no more under the direct control of the data owner. The shuffle index has been proposed as a solution for addressing these issues when data are stored at an external third party. In this paper, we extend the shuffle index with support for access control, that is, for enforcing authorizations on data. Our approach is based on the use of selective encryption and on the organization of data and authorizations in two shuffle indexes. Owners regulate access to their data through authorizations that allow different users to access different portions of the data, while, at the same time, the confidentiality of accesses is guaranteed. The proposed approach also supports update operations over the outsourced data collection (i.e., insertion, removal, and update) as well as of the access control policy (i.e., grant and revoke). Also, our approach protects the nature of each access operation, making revoke operations and resource removal operations indistinguishable by the storing server and/or observing users

Confidential database-as-a-service approaches: taxonomy and survey

Outsourcing data to external providers has gained momentum with the advent of cloud computing. Encryption allows data confidentiality to be preserved when outsourcing data to untrusted external providers that may be compromised by attackers. However, encryption has to be applied in a way that still allows the external provider to evaluate queries received from the client. Even though confidential database-as-a-service (DaaS) is still an active field of research, various techniques already address this problem, which we call confidentiality preserving indexing approaches (CPIs). CPIs make individual tradeoffs between the functionality provided, i.e., the types of queries that can be evaluated, the level of protection achieved, and performance.In this paper, we present a taxonomy of requirements that CPIs have to satisfy in deployment scenarios including the required functionality and the required level of protection against various attackers. We show that the taxonomy?s underlying principles serve as a methodology to assess CPIs, primarily by linking attacker models to CPI security properties. By use of this methodology, we survey and assess ten previously proposed CPIs. The resulting CPI catalog can help the reader who would like to build DaaS solutions to facilitate DaaS design decisions while the proposed taxonomy and methodology can also be applied to assess upcoming CPI approaches

Three-server swapping for access confidentiality

We propose an approach to protect confidentiality of data and accesses to them when data are stored and managed by external providers, and hence not under direct control of their owner. Our approach is based on the use of distributed data allocation among three independent servers and on a dynamic re-allocation of data at every access. Dynamic re-allocation is enforced by swapping data involved in an access across the servers in such a way that accessing a given node implies re-allocating it to a different server, then destroying the ability of servers to build knowledge by observing accesses. The use of three servers provides uncertainty, to the eyes of the servers, of the result of the swapping operation, even in presence of collusion among them

Selective and private access to outsourced data centers

The advancements in the Information Technology and the rapid diffusion of novel computing paradigms have accelerated the trend of moving data to the cloud. Public and private organizations are more often outsourcing their data centers to the cloud for economic and/or performance reasons, thus making data confidentiality an essential requirement. A basic technique for protecting data confidentiality relies on encryption: data are encrypted by the owner before their outsourcing. Encryption however complicates both the query evaluation and enforcement of access restrictions to outsourced data. In this chapter, we provide an overview of the issues and techniques related to the support of selective and private access to outsourced data in a scenario where the cloud provider is trusted for managing the data but not for reading their content. We therefore illustrate methods for enforcing access control and for efficiently and privately executing queries (at the server side) over encrypted data. We also show how the combined adoption of approaches supporting access control and for efficient query evaluation may cause novel privacy issues that need to be carefully handled