Oblivious Random Access Machine Research Articles

LPS-ORAM: Perfectly Secure Oblivious RAM with Logarithmic Bandwidth Overhead

Oblivious Random Access Machine (ORAM) is a cryptographic tool used to obfuscate the access pattern. In this paper, we focus on perfect security of ORAM. A perfectly secure ORAM is an ORAM that can resist against an adversary with unlimited computing power, and the failure probability of ORAM is zero rather than negligible. Since all existing perfectly secure single-server ORAM solutions require at least sublinear worst-case bandwidth overhead, we pose a natural and open question: can we construct a perfectly secure single-server ORAM with logarithmic worst-case bandwidth overhead? In this paper, we propose the first tree-based perfectly secure ORAM scheme, named LPS-ORAM. To meet the requirements of perfectly secure ORAM, two techniques are presented. One technique is dynamic remapping associated with a mutable scope, and the other is dynamically balanced eviction. Their combined effect allows the root bucket to never fill up while maintaining its statistical security in tree-based ORAM. In the worst case, our solution achieves logarithmic bandwidth overhead. Therefore, our solution answers the open question in the affirmative. In terms of overhead for temporary storage on the client side, compared with the latest perfectly secure ORAM solution, our solution is reduced from sublinear to logarithmic, and even if the server storage overhead scales lightly, it is still at the same level of quantity as the state of the art. Finally, the evaluation results show that our LPS-ORAM has a significant advantage in terms of bandwidth overhead and overhead for temporary storage on the client side.

A rORAM scheme with logarithmic bandwidth and logarithmic locality

Oblivious Random Access Machine (ORAM) is a kind of cryptographic primitive that allows a client to access its private data from the server without disclosing the access pattern. To deal with consecutive requested blocks at a time efficiently, range ORAM (rORAM) is presented. In the previous rORAM scheme, the locality, namely, the number of discontinuous seeks to complete a request, is reduced to O(log2 N), nevertheless, the bandwidth cost is increased to the poly-logarithmic level. Hence, there exists an open question, that is, whether rORAM can be constructed with the same bandwidth efficiency as a regular ORAM, that is, O(log N)-block? In this paper, we propose a new rORAM scheme, called L2-rORAM. In our scheme, a compatible superblock technique is proposed, and it is combined together with an eviction technique for range blocks, so that it avoids duplication of multiple copies and extra dummy access. As a result, it obtains O(log N)-block bandwidth cost, which affirmatively answers the above open question. Meanwhile, the data locality is reduced to O(log N). In addition, the client storage is maintained at the small level of O(log N)-block, and the server storage is maintained at the unexpanded level of O(N)-block. Finally, experimental results show that the average response time of our L2-rORAM is reduced by one order of magnitude over the state-of-the-art rORAM scheme.

Efficient Oblivious Data Structures for Database Services on the Cloud

Database-as-a-service (DBaaS) allows the client to store and manage structured data on the cloud remotely. Despite its merits, DBaaS also brings significant privacy issues. Existing encryption techniques (e.g., SQL-aware encryption) can mitigate privacy concerns, but they still leak information through access patterns, which are vulnerable to statistical inference attacks. Oblivious Random Access Machine (ORAM) can seal such leakages; however, the recent studies showed significant challenges on the integration of ORAM into databases. That is, the direct usage of ORAM on databases is not only costly but also permits very limited query functionalities. In this paper, we propose new oblivious data structures called Oblivious Matrix Structure (OMAT), which allow tree-based ORAM to be integrated into database systems in a more efficient manner with diverse query functionalities supported. OMAT provides special ORAM packaging strategies for table structures, which not only offers a significantly better performance but also enables a broad range of query types that may not be efficient in existing frameworks. On the other hand, OTREE allows oblivious conditional queries to be performed on tree-indexed databases more efficiently than existing techniques. We implemented our proposed techniques and evaluated their performance on a real cloud database with various metrics, compared with state-of-the-art counterparts.

A Tale of Two Trees: One Writes, and Other Reads

Abstract The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T 3, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access machine (ORAM) to protect SPV clients’ requests from potentially malicious nodes. The key novelty of T 3 lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output (UTXO) Set. Our experiment shows that T 3 is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.

Pbsx: A practical private boolean search using Intel SGX

Searchable encryption enables private keyword search over encrypted data held on an untrusted cloud. However, existing searchable encryption schemes promise search efficiency at the expense of additional privacy leakage or expensive storage overhead. Intel SGX is a promising tool to allow efficient and private keyword search. However, since the size of enclave created by SGX is limited, frequent sensitive-page swaps between the enclave and the untrusted memory cause severe performance degradation for large datasets. Moreover, SGX does not protect against access pattern leakages which are vulnerable to statistical inference attacks.To overcome the above problems, we present Pbsx, an efficient private boolean searchable encryption scheme using Intel SGX. Pbsx firstly analyzes and chooses the suitable index data structures (e.g. bitmap and bloom filter tree) for private boolean search in the context of Intel SGX. Pbsx then combines these index data structures with oblivious random access machine (ORAM) schemes to design oblivious index data structures (e.g. oblivious bitmap and oblivious bloom filter tree), such that it could provide storage efficiency without suffering from access pattern leakages. Since the direct composition of the index data structures and the ORAM schemes is extremely costly for search operations, we design auxiliary data structures (e.g. map table and id index tree) to mitigate the efficiency barrier. Based on these oblivious index data structures, we propose oblivious search algorithms which can achieve a worst-case sub-linear search time. We implement Pbsx and evaluate the performance of our constructions with various metrics. Comparing with previous schemes, the results indicate that Pbsx can achieve a 5~12x speed-up for the million-level dataset and a 13~51x speed-up for the small dataset. In addition, Pbsx also achieves less access pattern leakages.

A Multi-server ORAM Framework with Constant Client Bandwidth Blowup

Oblivious Random Access Machine (ORAM) allows a client to hide the access pattern when accessing sensitive data on a remote server. It is known that there exists a logarithmic communication lower bound on any passive ORAM construction, where the server only acts as the storage service. This overhead, however, was shown costly for some applications. Several active ORAM schemes with server computation have been proposed to overcome this limitation. However, they mostly rely on costly homomorphic encryptions, whose performance is worse than passive ORAM. In this article, we propose S 3 ORAM, a new multi-server ORAM framework, which features O (1) client bandwidth blowup and low client storage without relying on costly cryptographic primitives. Our key idea is to harness Shamir Secret Sharing and a multi-party multiplication protocol on applicable binary tree-ORAM paradigms. This strategy allows the client to instruct the server(s) to perform secure and efficient computation on his/her behalf with a low intervention thereby, achieving a constant client bandwidth blowup and low server computational overhead. Our framework can also work atop a general k -ary tree ORAM structure ( k ≥ 2). We fully implemented our framework, and strictly evaluated its performance on a commodity cloud platform (Amazon EC2). Our comprehensive experiments confirmed the efficiency of S 3 ORAM framework, where it is approximately 10× faster than the most efficient passive ORAM (i.e., Path-ORAM) for a moderate network bandwidth while being three orders of magnitude faster than active ORAM with O (1) bandwidth blowup (i.e., Onion-ORAM). We have open-sourced the implementation of our framework for public testing and adaptation.

Blockchain-based accountability for multi-party oblivious RAM

Recently, oblivious random access machine (ORAM) has been widely used to prevent privacy leakage from user’s access pattern. However, in multi-user scenarios, the obliviousness property of ORAM facilitates the malicious data modification by unauthorized users, which brings a new security challenge of user accountability to ORAM applications. Moreover, based on our observations, existing user accountability schemes for multi-user ORAM induce the extremely unacceptable overhead in both time and storage. What is worse, it is still inherent the traditional cloud accountability problem that the untrusted cloud server may have misbehavior on storing the outsourced data. In this paper, we focus on the issue that how to do accountability for both malicious users and untrusted cloud server without the independent trusted third party server.To address the above problem, we design and implement a Traceable Oblivious RAM, or T-ORAM for short, a cryptographic system that protects the privacy of users and the integrity of outsourced data based on group signatures. It can detect malicious users quickly by utilizing the traceability property of group signatures, and cost less storage overhead comparing with the existing solutions. Then, we further propose a more secure solution of Blockchain-based Traceable Oblivious RAM (BT-ORAM). Specifically, by introducing the blockchain technology, BT-ORAM can detect the malicious behavior from both malicious users and untrusted cloud server. BT-ORAM is the first accountability work for multi-user ORAM that deal with both malicious users and the untrusted cloud server. Finally, security analysis and experimental results show that our method outperforms the state-of-the-art accountability work for oblivious RAM, S-GORAM, in both security and performance.

A multi-server oblivious dynamic searchable encryption framework

Data privacy is one of the main concerns for data outsourcing on the cloud. Although standard encryption can provide confidentiality, it prevents the client from searching/retrieving meaningful information on the outsourced data thereby, degrading the benefits of using cloud services. To address th is data utilization versus privacy dilemma, Dynamic Searchable Symmetric Encryption (DSSE) has been proposed. DSSE enables encrypted search and update functionality over the encrypted data via a secure index. However, the state-of-the-art DSSE constructions leak information from the access pattern, making them vulnerable against various attacks. While generic Oblivious Random Access Machine (ORAM) can hide the access pattern, it incurs a heavy communication overhead, which was shown costly to be directly used in the DSSE setting. In this article, by exploiting the multi-cloud infrastructure, we develop a comprehensive Oblivious Distributed DSSE (ODSE) framework that allows oblivious search and updates on the encrypted index with high security and improved efficiency over the use of generic ORAM. Our framework contains a series of ODSE schemes each featuring different levels of performance and security required by various types of real-life applications. ODSE offers desirable security guarantees such as information-theoretic security and robustness in the presence of a malicious adversary. We fully implemented ODSE framework and evaluated its performance in a real cloud environment (Amazon EC2). Our experiments showed that ODSE schemes are 3×-57× faster than using generic ORAMs on a DSSE encrypted index under real network settings.

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

Abstract 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 performance 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 consumption 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.

Recursive Matrix Oblivious RAM: An ORAM Construction for Constrained Storage Devices

Oblivious random access machine (ORAM) constructions can be used to hide a client’s access pattern from a trusted but curious storage server. The privacy provided comes at the cost of increasing communication overhead, storage overhead, and computation overhead of the system. Recursive matrix-based ORAM (RM-ORAM) is a new ORAM construction, which is designed for constrained storage space devices. RM-ORAM significantly reduces the client storage usage by using recursion, while the computational and bandwidth overhead are slightly increased as a tradeoff. However, it can achieve better overall asymptotic performance than other existing ORAM schemes, e.g., recursive Path ORAM. In this paper, we present the construction and its theoretical analysis. In addition, we present how to select the appropriate number of data blocks, which are being downloaded per level of recursion and the appropriate size of reserved space on the client. We provide theoretical security and performance analysis, as well as experimental results to illustrate how RM-ORAM satisfies security requirements and provides improved performance compared with other ORAM schemes.

Software protection and simulation on oblivious RAMs

Software protection is one of the most important issues concerning computer practice. There exist many heuristics and ad-hoc methods for protection, but the problem as a whole has not received the theoretical treatment it deserves. In this paper, we provide theoretical treatment of software protection. We reduce the problem of software protection to the problem of efficient simulation on oblivious RAM. A machine is oblivious if thhe sequence in which it accesses memory locations is equivalent for any two inputs with the same running time. For example, an oblivious Turing Machine is one for which the movement of the heads on the tapes is identical for each computation. (Thus, the movement is independent of the actual input.) What is the slowdown in the running time of a machine, if it is required to be oblivious? In 1979, Pippenger and Fischer showed how a two-tape oblivious Turing Machine can simulate, on-line, a one-tape Turing Machine, with a logarithmic slowdown in the running time. We show an analogous result for the random-access machine (RAM) model of computation. In particular, we show how to do an on-line simulation of an arbitrary RAM by a probabilistic oblivious RAM with a polylogaithmic slowdown in the running time. On the other hand, we show that a logarithmic slowdown is a lower bound.