Optimizing Trees for Static Searchable Encryption

Searchable symmetric encryption (SSE) enables data owners to conduct searches over encrypted data stored by an untrusted server, retrieving only those encrypted files that match the search queries. Several recent schemes employ a server-side encrypted index in the form of a search tree where each node stores a bit vector denoting for each keyword whether any file in its subtree contains that keyword. Our work is motivated by the observation that the way data is distributed in such a search tree has a big impact on the cost of searches. For single-keyword queries, it impacts the number of different paths that must be followed to find all the matching files; for multi-keyword queries, the arrangement of the tree also impacts the number of nodes visited during the search on paths that do not lead to any satisfying data elements. We present three algorithms that improve the performance of SSE schemes based on tree indexes and prove that for cases where the search cost is high, the cost of our algorithms converges to the cost of the optimal tree. In our experiments, the resulting search trees outperform the arbitrary search trees used in previous works by a factor of up to tw

SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac

TimeClave: Oblivious In-enclave Time series Processing System

Cloud platforms are widely adopted by many systems, such as time series processing systems, to store and process massive amounts of sensitive time series data. Unfortunately, several incidents have shown that cloud platforms are vulnerable to internal and external attacks that lead to critical data breaches. Adopting cryptographic protocols such as homomorphic encryption and secure multi-party computation adds high computational and network overhead to query operations. We present TimeClave, a fully oblivious in-enclave time series processing system: TimeClave leverages Intel SGX to support aggregate statistics on time series with minimal memory consumption inside the enclave. To hide the access pattern inside the enclave, we introduce a non-blocking read-optimised ORAM named RoORAM. TimeClave integrates RoORAM to obliviously and securely handle client queries with high performance. With an aggregation time interval of $10s$, $2^{14}$ summarised data blocks and 8 aggregate functions, TimeClave run point query in $0.03ms$ and a range query of 50 intervals in $0.46ms$. Compared to the ORAM baseline, TimeClave achieves lower query latency by up to $2.5\times$ and up to $2\times$ throughput, with up to 22K queries per second.Comment: The short version of this paper has been accepted as a Full Paper in the International Conference on Information and Communications Security (ICICS) 202

Private Eyes: Zero-Leakage Iris Searchable Encryption

Biometric databases are being deployed with few cryptographic protections. Because of the nature of biometrics, privacy breaches affect users for their entire life. This work introduces Private Eyes, the first zero-leakage biometric database. The only leakage of the system is unavoidable: 1) the log of the dataset size and 2) the fact that a query occurred. Private Eyes is built from symmetric searchable encryption. Proximity queries are the required functionality: given a noisy reading of a biometric, the goal is to retrieve all stored records that are close enough according to a distance metric. Private Eyes combines locality sensitive-hashing or LSHs (Indyk and Motwani, STOC 1998) and encrypted maps. One searches for the disjunction of the LSHs of a noisy biometric reading. The underlying encrypted map needs to efficiently answer disjunction queries. We focus on the iris biometric. Iris biometric data requires a large number of LSHs, approximately 1000. The most relevant prior work is in zero-leakage k-nearest-neighbor search (Boldyreva and Tang, PoPETS 2021), but that work is designed for a small number of LSHs. Our main cryptographic tool is a zero-leakage disjunctive map designed for the setting when most clauses do not match any records. For the iris, on average at most 6% of LSHs match any stored value. To aid in evaluation, we produce a synthetic iris generation tool to evaluate sizes beyond available iris datasets. This generation tool is a simple generative adversarial network. Accurate statistics are crucial to optimizing the cryptographic primitives so this tool may be of independent interest. Our scheme is implemented and open-sourced. For the largest tested parameters of 5000 stored irises, search requires 26 rounds of communication and 26 minutes of single-threaded computation

Tunable Security for Deployable Data Outsourcing

Security mechanisms like encryption negatively affect other software quality characteristics like efficiency. To cope with such trade-offs, it is preferable to build approaches that allow to tune the trade-offs after the implementation and design phase. This book introduces a methodology that can be used to build such tunable approaches. The book shows how the proposed methodology can be applied in the domains of database outsourcing, identity management, and credential management

Oblivious Substring Search with Updates

We are the first to address the problem of efficient oblivious substring search over encrypted data supporting updates. Our two new protocols SA-ORAM and ST-ORAM obliviously search for substrings in an outsourced set of n encrypted strings. Both protocols are efficient, requiring communication complexity that is only poly-logarithmic in n. Compared to a straightforward solution for substring search using recent “oblivious data structures” [30], we demonstrate that our tailored solutions improve communication complexity by a factor of logn. The idea behind SA-ORAM and ST-ORAM is to employ a new, hierarchical ORAM tree structure that takes advantage of data dependency and optimizes the size of ORAM blocks and tree height. Based on oblivious suffix arrays, SA-ORAM targets efficiency, yet does not allow updates to the outsourced set of strings. ST-ORAM, based on oblivious suffix trees, allows updates at the additional communications cost of a factor of loglogn. We implement and benchmark SA-ORAM to show its feasibility for practical deployments: even for huge datasets of 2^40 strings, an oblivious substring search can be performed with only hundreds of KBytes communication cost

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

Towards a secure and efficient search over encrypted cloud data

Includes bibliographical references.2016 Summer.Cloud computing enables new types of services where the computational and network resources are available online through the Internet. One of the most popular services of cloud computing is data outsourcing. For reasons of cost and convenience, public as well as private organizations can now outsource their large amounts of data to the cloud and enjoy the benefits of remote storage and management. At the same time, confidentiality of remotely stored data on untrusted cloud server is a big concern. In order to reduce these concerns, sensitive data, such as, personal health records, emails, income tax and financial reports, are usually outsourced in encrypted form using well-known cryptographic techniques. Although encrypted data storage protects remote data from unauthorized access, it complicates some basic, yet essential data utilization services such as plaintext keyword search. A simple solution of downloading the data, decrypting and searching locally is clearly inefficient since storing data in the cloud is meaningless unless it can be easily searched and utilized. Thus, cloud services should enable efficient search on encrypted data to provide the benefits of a first-class cloud computing environment. This dissertation is concerned with developing novel searchable encryption techniques that allow the cloud server to perform multi-keyword ranked search as well as substring search incorporating position information. We present results that we have accomplished in this area, including a comprehensive evaluation of existing solutions and searchable encryption schemes for ranked search and substring position search