Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become mandatory in the design of new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has a poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient, and FASTIO achieves a much better scalability due to its optimized I/O

Hardware-Supported ORAM in Effect: Practical Oblivious Search and Update on Very Large Dataset

The ability to query and update over encrypted data is an essential feature to enable breach- resilient cyber-infrastructures. Statistical attacks on searchable encryption (SE) have demonstrated the importance of sealing information leaks in access patterns. In response to such attacks, the community has proposed the Oblivious Random Access Machine (ORAM). However, due to the logarithmic communication overhead of ORAM, the composition of ORAM and SE is known to be costly in the conventional client-server model, which poses a critical barrier toward its practical adaptations. In this paper, we propose a novel hardware-supported privacy-enhancing platform called Practical Oblivious Search and Update Platform (POSUP), which enables oblivious keyword search and update operations on large datasets with high efficiency. We harness Intel SGX to realize efficient oblivious data structures for oblivious search/update purposes. We implemented POSUP and evaluated its per- formance on a Wikipedia dataset containing ≥ 229 keyword-file pairs. Our implementation is highly efficient, taking only 1 ms to access a 3 KB block with Circuit-ORAM. Our experiments have shown that POSUP offers up to 70× less end-to-end delay with 100× reduced network bandwidth consump- tion compared with the traditional ORAM-SE composition without secure hardware. POSUP is also at least 4.5× faster for up to 99.5% of keywords that can be searched compared with state-of-the-art Intel SGX-assisted search platforms

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

A survey of advanced encryption for database security: primitives, schemes, and attacks

The use of traditional encryption techniques in Database Management Systems is limited, as encrypting data within the database can prevent basic functionalities such as ordering and searching. Advanced encryption techniques and trusted hardware, however, can enable standard functionalities to be achieved on encrypted databases, and a number of such schemes have been proposed in the recent literature. In this survey, different approaches towards database security through software/hardware components are explored and compared based on performance and security, and relevant attacks are discussed

Privacy-preserving efficient searchable encryption

Data storage and computation outsourcing to third-party managed data centers, in environments such as Cloud Computing, is increasingly being adopted by individuals, organizations, and governments. However, as cloud-based outsourcing models expand to society-critical data and services, the lack of effective and independent control over security and privacy conditions in such settings presents significant challenges. An interesting solution to these issues is to perform computations on encrypted data, directly in the outsourcing servers. Such an approach benefits from not requiring major data transfers and decryptions, increasing performance and scalability of operations. Searching operations, an important application case when cloud-backed repositories increase in number and size, are good examples where security, efficiency, and precision are relevant requisites. Yet existing proposals for searching encrypted data are still limited from multiple perspectives, including usability, query expressiveness, and client-side performance and scalability. This thesis focuses on the design and evaluation of mechanisms for searching encrypted data with improved efficiency, scalability, and usability. There are two particular concerns addressed in the thesis: on one hand, the thesis aims at supporting multiple media formats, especially text, images, and multimodal data (i.e. data with multiple media formats simultaneously); on the other hand the thesis addresses client-side overhead, and how it can be minimized in order to support client applications executing in both high-performance desktop devices and resource-constrained mobile devices. From the research performed to address these issues, three core contributions were developed and are presented in the thesis: (i) CloudCryptoSearch, a middleware system for storing and searching text documents with privacy guarantees, while supporting multiple modes of deployment (user device, local proxy, or computational cloud) and exploring different tradeoffs between security, usability, and performance; (ii) a novel framework for efficiently searching encrypted images based on IES-CBIR, an Image Encryption Scheme with Content-Based Image Retrieval properties that we also propose and evaluate; (iii) MIE, a Multimodal Indexable Encryption distributed middleware that allows storing, sharing, and searching encrypted multimodal data while minimizing client-side overhead and supporting both desktop and mobile devices

Practical yet Provably Secure: Complex Database Query Execution over Encrypted Data

Encrypted databases provide security for outsourced data. In this work novel encryption schemes supporting different database query types are presented enabling complex database queries over encrypted data. For specific constructions enabling exact keyword queries, range queries, database joins and substring queries over encrypted data we prove security in a formal framework, present a theoretical runtime analysis and provide an assessment of practical performance characteristics