Analysis of outsourcing data to the cloud using autonomous key generation

Cloud computing, a technology that enables users to store and manage their data at a low cost and high availability, has been emerging for the past few decades because of the many services it provides. One of the many services cloud computing provides to its users is data storage. The majority of the users of this service are still concerned to outsource their data due to the integrity and confidentiality issues, as well as performance and cost issues, that come along with it. These issues make it necessary to encrypt data prior to outsourcing it to the cloud. However, encrypting data prior to outsourcing makes searching the data obsolete, lowering the functionality of the cloud. Most existing cloud storage schemes often prioritize security over performance and functionality, or vice versa. In this thesis, the cloud storage service is explored, and the aspects of security, performance, and functionality are analyzed in order to investigate the trade-offs of the service. DSB-SEIS, a scheme with encryption intensity selection, an autonomous key generation algorithm that allows users to control the encryption intensity of their files, as well as other features is developed in order to find a balance between performance, security, and functionality. The features that DSB-SEIS contains are deduplication, assured deletion, and searchable encryption. The effect of encryption intensity selection on encryption, decryption, and key generation is explored, and the performance and security of DSB-SEIS are evaluated. The MapReduce framework is also used to investigate the DSB-SEIS algorithm performance with big data. Analysis demonstrates that the encryption intensity selection algorithm generates a manageable number of encryption keys based on the confidentiality of data while not adding significant overhead on encryption or decryption --Abstract, page iii

An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution

Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews

S2Dedup: SGX-enabled Secure Deduplication

Secure deduplication allows removing duplicate content at third-party storage services while preserving the privacy of users’ data. However, current solutions are built with strict designs that cannot be adapted to storage service and applications with different security and performance requirements. We present S2Dedup, a trusted hardware-based privacy-preserving deduplication system designed to support multiple security schemes that enable different levels of performance, security guarantees and space savings. An in-depth evaluation shows these trade-offs for the distinct Intel SGX-based secure schemes supported by our prototype. Moreover, we propose a novel Epoch and Exact Frequency scheme that prevents frequency analysis leakage attacks present in current deterministic approaches for secure deduplication while maintaining similar performance and space savings to state-of-the-art approaches

Sharing of Data Using Key Aggregation and Searchable Encryption

Sharing data with different users is an important functionality of the cloud. However, while enjoying the convenience provided by the cloud storage, user’s main concern is regarding the data leakage present in cloud. A promising approach to prevent this is encryption of data before uploading onto cloud. The desire to selectively and securely share documents with any group of users demands different documents to have different encryption keys. This necessitates the distribution of a large number of keys to users for both encryption and search, those users will have to securely store these keys, and submit an equally large number of keyword trapdoors to the cloud in order to perform search. In this paper, we resolve this problem by extending the concept of Key Aggregate Searchable Encryption (KASE) scheme which employs a single aggregate key and a single trapdoor. Here, the data owner only needs to distribute a single key to a user for sharing a large number of documents, and the user only needs to submit a single trapdoor to the cloud for querying the documents. Also, we provide a functionality of selection of keyword based on their rank by the Data owner in such a way that the selected keywords describe the file. Thus, this scheme makes the management of the keys efficient and also makes the sharing of documents over the cloud more secure

Secure Batch Deduplication Without Dual Servers in Backup System

Cloud storage provides highly available and low cost resources to users. However, as massive amounts of outsourced data grow rapidly, an effective data deduplication scheme is necessary. This is a hot and challenging field, in which there are quite a few researches. However, most of previous works require dual-server fashion to be against brute-force attacks and do not support batch checking. It is not practicable for the massive data stored in the cloud. In this paper, we present a secure batch deduplication scheme for backup system. Besides, our scheme resists the brute-force attacks without the aid of other servers. The core idea of the batch deduplication is to separate users into different groups by using short hashes. Within each group, we leverage group key agreement and symmetric encryption to achieve secure batch checking and semantically secure storage. We also extensively evaluate its performance and overhead based on different datasets. We show that our scheme saves the data storage by up to 89.84%. These results show that our scheme is efficient and scalable for cloud backup system and can also ensure data confidentiality

CacheZoom: How SGX Amplifies The Power of Cache Attacks

In modern computing environments, hardware resources are commonly shared, and parallel computation is widely used. Parallel tasks can cause privacy and security problems if proper isolation is not enforced. Intel proposed SGX to create a trusted execution environment within the processor. SGX relies on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards side-channel attacks. We introduce a powerful cache side-channel attack that provides system adversaries a high resolution channel. Our attack tool named CacheZoom is able to virtually track all memory accesses of SGX enclaves with high spatial and temporal precision. As proof of concept, we demonstrate AES key recovery attacks on commonly used implementations including those that were believed to be resistant in previous scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous works which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover AES keys from T-Table based implementations with as few as ten measurements.Comment: Accepted at Conference on Cryptographic Hardware and Embedded Systems (CHES '17

Efficient Cloud Backup and Private Search

As organizations and companies are increasingly offloading data and computation to the cloud to reduce infrastructure administration,data volume keeps growing and new services and algorithms are needed to meet increasing demands for both storage capacity and privacy.The first part of my thesis will address cloud data backup.Organizations and companies often backup and archive high volumes of binary and text datasets for fault tolerance,internal investigation, and electronic discovery. Source-side deduplication has an advantage to avoid or minimize duplicated data transmitted over the network,however it demands more computing resource to perform extensive fingerprint comparison which would otherwise be available for primary services at the source.For data stored in the cloud, users need efficient, scalable services for searching these files.In the first part of this thesis, I will cover the key components of existing solutions for large-scale backup storage in the cloud.I will go into detail on how deduplication is important to large scale backup systems, and review some ongoing work.I will also detail my contributions in this area towards low-profile source-side deduplication.The second part of my thesis addresses an open problem for efficient private document search on data hosted on the cloud.As sensitive information is increasingly centralized into the cloud, for the protection of data privacy, such data is often encrypted,which makes effective data indexing and search a very challenging task. To overcome the challenges of querying encrypted datasets,searchable encryption schemes allow users to securely search over encrypted data through keywords.No existing solutions for efficient ranking which involves complex arithmetic computation in feature composition and scoring currently exist,and without relevant ranking of search results queries over very large datasets which may return many results can be impractical.In the second part of my thesis I will review existing work on private search and introduce our ongoing and published work for this open problem,focusing on how to make private search practical and scalable for large datasets

Secure and efficient processing of outsourced data structures using trusted execution environments

In recent years, more and more companies make use of cloud computing; in other words, they outsource data storage and data processing to a third party, the cloud provider. From cloud computing, the companies expect, for example, cost reductions, fast deployment time, and improved security. However, security also presents a significant challenge as demonstrated by many cloud computing–related data breaches. Whether it is due to failing security measures, government interventions, or internal attackers, data leakages can have severe consequences, e.g., revenue loss, damage to brand reputation, and loss of intellectual property. A valid strategy to mitigate these consequences is data encryption during storage, transport, and processing. Nevertheless, the outsourced data processing should combine the following three properties: strong security, high efficiency, and arbitrary processing capabilities. Many approaches for outsourced data processing based purely on cryptography are available. For instance, encrypted storage of outsourced data, property-preserving encryption, fully homomorphic encryption, searchable encryption, and functional encryption. However, all of these approaches fail in at least one of the three mentioned properties. Besides approaches purely based on cryptography, some approaches use a trusted execution environment (TEE) to process data at a cloud provider. TEEs provide an isolated processing environment for user-defined code and data, i.e., the confidentiality and integrity of code and data processed in this environment are protected against other software and physical accesses. Additionally, TEEs promise efficient data processing. Various research papers use TEEs to protect objects at different levels of granularity. On the one end of the range, TEEs can protect entire (legacy) applications. This approach facilitates the development effort for protected applications as it requires only minor changes. However, the downsides of this approach are that the attack surface is large, it is difficult to capture the exact leakage, and it might not even be possible as the isolated environment of commercially available TEEs is limited. On the other end of the range, TEEs can protect individual, stateless operations, which are called from otherwise unchanged applications. This approach does not suffer from the problems stated before, but it leaks the (encrypted) result of each operation and the detailed control flow through the application. It is difficult to capture the leakage of this approach, because it depends on the processed operation and the operation’s location in the code. In this dissertation, we propose a trade-off between both approaches: the TEE-based processing of data structures. In this approach, otherwise unchanged applications call a TEE for self-contained data structure operations and receive encrypted results. We examine three data structures: TEE-protected B+-trees, TEE-protected database dictionaries, and TEE-protected file systems. Using these data structures, we design three secure and efficient systems: an outsourced system for index searches; an outsourced, dictionary-encoding–based, column-oriented, in-memory database supporting analytic queries on large datasets; and an outsourced system for group file sharing supporting large and dynamic groups. Due to our approach, the systems have a small attack surface, a low likelihood of security-relevant bugs, and a data owner can easily perform a (formal) code verification of the sensitive code. At the same time, we prevent low-level leakage of individual operation results. For all systems, we present a thorough security evaluation showing lower bounds of security. Additionally, we use prototype implementations to present upper bounds on performance. For our implementations, we use a widely available TEE that has a limited isolated environment—Intel Software Guard Extensions. By comparing our systems to related work, we show that they provide a favorable trade-off regarding security and efficiency