Conclave

Volgushev, Nikolaj; Schwarzkopf, Malte; Getchell, Ben; Varia, Mayank; Lapets, Andrei; Bestavros, Azer

doi:10.1145/3302424.3303982

Cited by 79 publications

(15 citation statements)

References 46 publications

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“…Many cryptographic SMC protocols can be leveraged to design SMQ protocols [13,145]; however, they are still impractical because of either the huge communication cost or the extensive computational cost. In this survey, we focus on the hardware-assisted approaches, which improve efficiency significantly.…”

Section: A3 Secure Multiparty Queriesmentioning

confidence: 99%

A Survey of Secure Computation Using Trusted Execution Environments

Li¹,

Yang²,

Xiang³

et al. 2023

Preprint

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As an essential technology underpinning trusted computing, the trusted execution environment (TEE) allows one to launch computation tasks on both on-and off-premises data while assuring confidentiality and integrity. This article provides a systematic review and comparison of TEE-based secure computation protocols. We first propose a taxonomy that classifies secure computation protocols into three major categories, namely secure outsourced computation, secure distributed computation and secure multi-party computation. To enable a fair comparison of these protocols, we also present comprehensive assessment criteria with respect to four aspects: setting, methodology, security and performance. Based on these criteria, we review, discuss and compare the state-of-the-art TEE-based secure computation protocols for both general-purpose computation functions and special-purpose ones, such as privacy-preserving machine learning and encrypted database queries. To the best of our knowledge, this article is the first survey to review TEE-based secure computation protocols and the comprehensive comparison can serve as a guideline for selecting suitable protocols for deployment in practice. Finally, we also discuss several future research directions and challenges.CCS Concepts: • Security and privacy → Tamper-proof and tamper-resistant designs; Trust frameworks; Privacy-preserving protocols; Management and querying of encrypted data.

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Section: A3 Secure Multiparty Queriesmentioning

confidence: 99%

A Survey of Secure Computation Using Trusted Execution Environments

Li¹,

Yang²,

Xiang³

et al. 2023

Preprint

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“…However, the performance shown in the experiment is several orders of magnitude slower than that of the plain text query; hence, it is still far from practical application. The Conclave system released in 2019 can implement secure queries between two or three data owners on the basis of OblivC and Sharemind and extend the underlying database of the data federation to big data processing engines such as Spark [2] . The system optimizes the query plan through push-down plain text calculation, thereby reducing the number of secure multiparty computation operations.…”

Section: Data Federated Join Querymentioning

confidence: 99%

“…Through the budget values, the corresponding Orders 2-4 can be found. Therefore, for the commodity tuple (1,11), the order tuples that can be joined are (2,17), (3,23), and (4, 29). The above tuples are sent to the central server for concatenation, and the first three rows of the join result shown on the right side of Figure 3 can be obtained.…”

mentioning

confidence: 99%

Secure Multi-party θ-join Algorithms Toward Data Federation

Zhang¹,

Li²,

Shi³

et al. 2023

International Journal of Software and Informatics

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Recently, many countries and regions have enacted data security policies, such as the General Data Protection Regulation proposed by the EU. The release of related laws and regulations has aggravated the problem of data silos, which makes it difficult to share data among various data owners. Data federation is a possible solution to this problem. Data federation refers to the calculation of query tasks jointly performed by multiple data owners without original data leaks using privacy computing technologies such as secure multi-party computing. This concept has become a research trend in recent years, and a series of representative systems have been proposed, such as SMCQL and Conclave. However, for the core join queries in the relational database system, the existing data federation system still has the following problems. First of all, the join query type is single, which is difficult to meet the query requirements under complex join conditions. Secondly, the algorithm performance has huge improvement space because the existing systems often call the security tool library directly, which means the runtime and communication overhead is high. Therefore, this paper proposes a join algorithm under data federation to address the above issues. The main contributions of this paper are as follows: firstly, multi-party-oriented federation security operators are designed and implemented, which can support many operations. Secondly, a federated θ-join algorithm and an optimization strategy are proposed to significantly reduce the security computation cost. Finally, the performance of the algorithm proposed in this paper is verified by the benchmark dataset TPC-H. The experimental results show that the proposed algorithm can reduce the runtime and communication overhead by 61.33% and 95.26%, respectively, compared with the existing data federation systems SMCQL and Conclave.

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“…It general, it can be said that the performance of all secure multiparty computing methods is determined by the number of messages exchanged between the parties involved, the required number of rounds of communication between the parties and the computational overhead per round. It should be noted, however, that the exact increase in computational complexity depends on the particular type of method used [45]and the operation performed [46].…”

Section: Axis 23: Scalabilitymentioning

confidence: 99%

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Wirth

Meurers

Johns

et al. 2021

BMC Med Inform Decis Mak

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Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

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Conclave

Cited by 79 publications

References 46 publications

A Survey of Secure Computation Using Trusted Execution Environments

A Survey of Secure Computation Using Trusted Execution Environments

Secure Multi-party θ-join Algorithms Toward Data Federation

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

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