Fully homomorphic encryption (FHE) enables a simple, attractive framework for secure search. Compared to other secure search systems, no costly setup procedure is necessary; it is sufficient for the client merely to upload the encrypted database to the server. Confidentiality is provided because the server works only on the encrypted query and records. While the search functionality is enabled by the full homomorphism of the encryption scheme. For this reason, researchers have been paying increasing attention to this problem. Since Akavia et al. (CCS 2018) presented a framework for secure search on FHE encrypted data and gave a working implementation called SPiRiT, several more efficient realizations have been proposed. In this paper, we identify the main bottlenecks of this framework and show how to significantly improve the performance of FHE-base secure search. In particular, To retrieve l matching items, the existing framework needs to repeat the protocol l times sequentially. In our new framework, all matching items are retrieved in parallel in a single protocol execution. The most recent work by Wren et al. (CCS 2020) requires O(n) multiplications to compute the first matching index. Our solution requires no homomorphic multiplication, instead using only additions and scalar multiplications to encode all matching indices. Our implementation and experiments show that to fetch 16 matching records, our system gives an 1800X speed-up over the state of the art in fetching the query results resulting in a 26X speed-up for the full search functionality.

Cloud-based electronic health records (EHR) systems provide important security controls by encrypting patient data. However, these records cannot be queried without decrypting the entire record. This incurs a huge amount of burden in network bandwidth and the client-side computation. As the volume of cloud-based EHRs reaches Big Data levels, it is essential to search over these encrypted patient records without decrypting them to ensure that the medical caregivers can efficiently access the EHRs. This is especially critical if the caregivers have access to only certain sections of the patient EHR and should not decrypt the whole record. In this paper, we present our novel approach that facilitates searchable encryption of large EHR systems using Attribute-based Encryption (ABE) and multi-keyword search techniques. Our framework outsources key search features to the cloud side. This way, our system can perform keyword searches on encrypted data with significantly reduced costs of network bandwidth and client-side computation.

Abstract We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size. We begin with the well-known LogLog sketch for computing the number of unique elements in a data stream. We show that this algorithm already achieves differential privacy (even without adding any noise) when computed using a private hash function by a trusted curator. Next, we show how to eliminate this requirement of a private hash function by injecting a small amount of noise, allowing us to instantiate an efficient LogLog protocol for the multi-party setting. To demonstrate the practicality of this approach, we run extensive experimentation on multiple data sets, including the publicly available IP address data set from University of Michigan’s scans of internet IPv4 space, to determine the trade-offs among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes. Finally, we generalize our approach for the LogLog sketch and obtain a general framework for constructing multi-party differentially private protocols for several other sketching algorithms.

Write-Only Oblivious RAM (WoORAM) protocols provide privacy by encrypting the contents of data and also hiding the pattern of write operations over that data. WoORAMs provide better privacy than plain encryption and better performance than more general ORAM schemes (which hide both writing and reading access patterns), and the write-oblivious setting has been applied to important applications of cloud storage synchronization and encrypted hidden volumes. In this paper, we introduce an entirely new technique for Write-Only ORAM, called DetWoORAM. Unlike previous solutions, DetWoORAM uses a deterministic, sequential writing pattern without the need for any "stashing" of blocks in local state when writes fail. Our protocol, while conceptually simple, provides substantial improvement over prior solutions, both asymptotically and experimentally. In particular, under typical settings the DetWoORAM writes only 2 blocks (sequentially) to backend memory for each block written to the device, which is optimal. We have implemented our solution using the BUSE (block device in user-space) module and tested DetWoORAM against both an encryption only baseline of dm-crypt and prior, randomized WoORAM solutions, measuring only a 3x-14x slowdown compared to an encryption-only baseline and around 6x-19x speedup compared to prior work.

Consumers want to ensure that their enterprise data is stored securely and obliviously on the cloud, such that the data objects or their access patterns are not revealed to anyone, including the cloud provider, in the public cloud environment. We have created a detailed ontology describing the oblivious cloud storage models and role based access controls that should be in place to manage this risk. We have developed an algorithm to store cloud data using oblivious data structure defined in this paper. We have also implemented the ObliviCloudManager application that allows users to manage their cloud data by validating it before storing it in an oblivious data structure. Our application uses role-based access control model and collection based document management to store and retrieve data efficiently. Cloud consumers can use our system to define policies for storing data obliviously and manage storage on untrusted cloud platforms even if they are unfamiliar with the underlying technology and concepts of oblivious data structures.

Recently there has been much interest in performing search queries over encrypted data to enable functionality while protecting sensitive data. One particularly efficient mechanism for executing such queries is order-preserving encryption/encoding (OPE) which results in ciphertexts that preserve the relative order of the underlying plaintexts thus allowing range and comparison queries to be performed directly on ciphertexts. Recently, Popa et al. (SP 2013) gave the first construction of an ideally-secure OPE scheme and Kerschbaum (CCS 2015) showed how to achieve the even stronger notion of frequency-hiding OPE. However, as Naveed et al. (CCS 2015) have recently demonstrated, these constructions remain vulnerable to several attacks. Additionally, all previous ideal OPE schemes (with or without frequency-hiding) either require a large round complexity of O(log n) rounds for each insertion, or a large persistent client storage of size O(n), where n is the number of items in the database. It is thus desirable to achieve a range query scheme addressing both issues gracefully. In this paper, we propose an alternative approach to range queries over encrypted data that is optimized to support insert-heavy workloads as are common in "big data" applications while still maintaining search functionality and achieving stronger security. Specifically, we propose a new primitive called partial order preserving encoding (POPE) that achieves ideal OPE security with frequency hiding and also leaves a sizable fraction of the data pairwise incomparable. Using only O(1) persistent and O(ne) non-persistent client storage for 0<e<1, our POPE scheme provides extremely fast batch insertion consisting of a single round, and efficient search with O(1) amortized cost for up to O(n(1-e)) search queries. This improved security and performance makes our scheme better suited for today's insert-heavy databases.

We present a new oblivious RAM that supports variable-sized storage blocks (vORAM), which is the first ORAM to allow varying block sizes without trivial padding. We also present a new history-independent data structure (a HIRB tree) that can be stored within a vORAM. Together, this construction provides an efficient and practical oblivious data structure (ODS) for a key/value map, and goes further to provide an additional privacy guarantee as compared to prior ODS maps: even upon client compromise, deleted data and the history of old operations remain hidden to the attacker. We implement and measure the performance of our system using Amazon Web Services, and the single-operation time for a realistic database (up to 256K entries) is less than 1 second. This represents a 100x speed-up compared to the current best oblivious map data structure (which provides neither secure deletion nor history independence) by Wang et al. (CCS 14).

Query privacy in secure DBMS is an important feature, although rarely formally considered outside the theoretical community. Because of the high overheads of guaranteeing privacy in complex queries, almost all previous works addressing practical applications consider limited queries (e.g., just keyword search), or provide a weak guarantee of privacy. In this work, we address a major open problem in private DB: efficient sub linear search for arbitrary Boolean queries. We consider scalable DBMS with provable security for all parties, including protection of the data from both server (who stores encrypted data) and client (who searches it), as well as protection of the query, and access control for the query. We design, build, and evaluate the performance of a rich DBMS system, suitable for real-world deployment on today medium-to large-scale DBs. On a modern server, we are able to query a formula over 10TB, 100M-record DB, with 70 searchable index terms per DB row, in time comparable to (insecure) MySQL (many practical queries can be privately executed with work 1.2-3 times slower than MySQL, although some queries are costlier). We support a rich query set, including searching on arbitrary boolean formulas on keywords and ranges, support for stemming, and free keyword searches over text fields. We identify and permit a reasonable and controlled amount of leakage, proving that no further leakage is possible. In particular, we allow leakage of some search pattern information, but protect the query and data, provide a high level of privacy for individual terms in the executed search formula, and hide the difference between a query that returned no results and a query that returned a very small result set. We also support private and complex access policies, integrated in the search process so that a query with empty result set and a query that fails the policy are hard to tell apart.