scholarly journals StealthDB: a Scalable Encrypted Database with Full SQL Query Support

2019 ◽  
Vol 2019 (3) ◽  
pp. 370-388 ◽  
Author(s):  
Dhinakaran Vinayagamurthy ◽  
Alexey Gribov ◽  
Sergey Gorbunov
Keyword(s):  
High Performance ◽  
Trusted Computing ◽  
Database Systems ◽  
Sensitive Data ◽  
Garbled Circuits ◽  
Cryptographic Algorithms ◽  
Trusted Computing Base ◽  
Encrypted Database ◽  
Security Guarantees ◽  
Sql Query

Abstract Encrypted database systems provide a great method for protecting sensitive data in untrusted infrastructures. These systems are built using either special-purpose cryptographic algorithms that support operations over encrypted data, or by leveraging trusted computing co-processors. Strong cryptographic algorithms (e.g., public-key encryptions, garbled circuits) usually result in high performance overheads, while weaker algorithms (e.g., order-preserving encryption) result in large leakage profiles. On the other hand, some encrypted database systems (e.g., Cipherbase, TrustedDB) leverage non-standard trusted computing devices, and are designed to work around the architectural limitations of the specific devices used. In this work we build StealthDB – an encrypted database system from Intel SGX. Our system can run on any newer generation Intel CPU. StealthDB has a very small trusted computing base, scales to large transactional workloads, requires minor DBMS changes, and provides a relatively strong security guarantees at steady state and during query execution. Our prototype on top of Postgres supports the full TPC-C benchmark with a 30% decrease in the average throughput over an unmodified version of Postgres operating on a 2GB unencrypted dataset.

scholarly journals Protecting Security-Sensitive Data Using Program Transformation and Trusted Execution Environment

2021 ◽  
Author(s):  
Anter Abdu Alhag Ali Faree ◽  
Yongzhi Wang
Keyword(s):  
Program Transformation ◽  
Trusted Computing ◽  
Control Flow ◽  
Public Cloud ◽  
Sensitive Data ◽  
New Approach ◽  
The Public ◽  
Trusted Computing Base ◽  
Execution Environment ◽  
Trusted Execution Environment

Abstract Cloud computing allows clients to upload their sensitive data to the public cloud and perform sensitive computations in those untrusted areas, which drives to possible violations of the confidentiality of client sensitive data. Utilizing Trusted Execution Environments (TEEs) to protect data confidentiality from other software is an effective solution. TEE is supported by different platforms, such as Intel’s Software Guard Extension (SGX). SGX provides a TEE, called an enclave, which can be used to protect the integrity of the code and the confidentiality of data. Some efforts have proposed different solutions in order to isolate the execution of security-sensitive code from the rest of the application. Unlike our previous work, CFHider, a hardware-assisted method that aimed to protect only the confidentiality of control flow of applications, in this study, we develop a new approach for partitioning applications into security-sensitive code to be run in the trusted execution setting and cleartext code to be run in the public cloud setting. Our approach leverages program transformation and TEE to hide security-sensitive data of the code. We describe our proposed solution by combining the partitioning technique, program transformation, and TEEs to protect the execution of security-sensitive data of applications. Some former works have shown that most applications can run in their entirety inside trusted areas such as SGX enclaves, and that leads to a large Trusted Computing Base (TCB). Instead, we analyze three case studies, in which we partition real Java applications and employ the SGX enclave to protect the execution of sensitive statements, therefore reducing the TCB. We also showed the advantages of the proposed solution and demonstrated how the confidentiality of security-sensitive data is protected.

scholarly journals A Hardware Platform for Ensuring OS Kernel Integrity on RISC-V

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2068
Author(s):  
Donghyun Kwon ◽  
Dongil Hwang ◽  
Yunheung Paek
Keyword(s):  
Side Effects ◽  
High Performance ◽  
Trusted Computing ◽  
Host System ◽  
Monitoring Task ◽  
Hardware Interface ◽  
Internal States ◽  
Trusted Computing Base ◽  
And Performance ◽  
Hardware Designs

The OS kernel is typically preassumed as a trusted computing base in most computing systems. However, it also implies that once an attacker takes control of the OS kernel, the attacker can seize the entire system. Because of such security importance of the OS kernel, many works have proposed security solutions for the OS kernel using an external hardware module located outside the processor. By doing this, these works can realize the physical isolation of security solutions from the OS kernel running in the processor, but they cannot access the inner state of the processor, which attackers can manipulate. Thus, they elaborated several methods to overcome such limited capability of external hardware. However, those methods usually come with several side effects, such as high-performance overhead, kernel code modifications, and/or excessively complicated hardware designs. In this paper, we introduce RiskiM, a new hardware-based monitoring platform to ensure kernel integrity from outside the host system. To deliver the inner state of the host to RiskiM, we have devised a hardware interface architecture, called PEMI. Through PEMI, RiskiM is supplied with all internal states of the host system essential for fulfilling its monitoring task to protect the kernel. To empirically validate our monitoring platform’s security strength and performance, we have fully implemented PEMI and RiskiM on a RISC-V based processor and FPGA, respectively. Our experiments show that RiskiM succeeds in the host kernel protection by detecting even the advanced attacks which could circumvent previous solutions, yet suffering from virtually no aforementioned side effects.

scholarly journals Threats and Vulnerabilities to IoT End Devices Architecture and suggested remedies

2020 ◽  
Vol 8 (6) ◽  
pp. 5712-5718
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Data Processing ◽  
Trusted Computing ◽  
External World ◽  
Security Threats ◽  
Iot Applications ◽  
Trusted Computing Base ◽  
Chain Of Trust ◽  
New Challenges

Due to decentralization of Internet of Things(IoT) applications and anything, anytime, anywhere connectivity has increased burden of data processing and decision making at IoT end devices. This overhead initiated new bugs and vulnerabilities thus security threats are emerging and presenting new challenges on these end devices. IoT End Devices rely on Trusted Execution Environments (TEEs) by implementing Root of trust (RoT) as soon as power is on thus forming Chain of trust (CoT) to ensure authenticity, integrity and confidentiality of every bit and byte of Trusted Computing Base (TCB) but due to un-trusted external world connectivity and security flaws such as Spectre and meltdown vulnerabilities present in the TCB of TEE has made CoT unstable and whole TEE are being misutilized. This paper suggests remedial solutions for the threats arising due to bugs and vulnerabilities present in the different components of TCB so as to ensure the stable CoT resulting into robust TEE.

scholarly journals Accurate Filtering of Privacy-Sensitive Information in Raw Genomic Data

2018 ◽  
Author(s):  
Jérémie Decouchant ◽  
Maria Fernandes ◽  
Marcus Völp ◽  
Francisco M Couto ◽  
Paulo Esteves-Veríssimo
Keyword(s):  
High Performance ◽  
Genomic Data ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Variable Regions ◽  
Fine Grained ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
Human Genomes ◽  
Long Reads

AbstractSequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data entails enormous risks if not protected to the highest standards. In this article, we follow the position and argue that post-alignment privacy is not enough and that data should be automatically protected as early as possible in the genomics workflow, ideally immediately after the data is produced. We show that a previous approach for filtering short reads cannot extend to long reads and present a novel filtering approach that classifies raw genomic data (i.e., whose location and content is not yet determined) into privacy-sensitive (i.e., more affected by a successful privacy attack) and non-privacy-sensitive information. Such a classification allows the fine-grained and automated adjustment of protective measures to mitigate the possible consequences of exposure, in particular when relying on public clouds. We present the first filter that can be indistinctly applied to reads of any length, i.e., making it usable with any recent or future sequencing technologies. The filter is accurate, in the sense that it detects all known sensitive nucleotides except those located in highly variable regions (less than 10 nucleotides remain undetected per genome instead of 100,000 in previous works). It has far less false positives than previously known methods (10% instead of 60%) and can detect sensitive nucleotides despite sequencing errors (86% detected instead of 56% with 2% of mutations). Finally, practical experiments demonstrate high performance, both in terms of throughput and memory consumption.

Gradual GRAM and secure computation for RAM programs

2021 ◽  
pp. 1-33
Author(s):  
Carmit Hazay ◽  
Mor Lilintal
Keyword(s):  
Secure Computation ◽  
Boolean Circuits ◽  
Conceptual Similarity ◽  
Garbled Circuits ◽  
Security Guarantees

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.

Trusted Cloud Computing

2019 ◽  
pp. 12-27
Keyword(s):  
Service Providers ◽  
Trusted Computing ◽  
Cloud Service ◽  
Sensitive Data ◽  
Remote Attestation ◽  
Research Directions ◽  
Computing Paradigm ◽  
Secure Computing ◽  
Cloud Service Providers ◽  
The Given

This chapter introduces various ideas to deal with insider attacks using the research directions, which are discussed in earlier chapters such as remote attestation, sealed storage, and integrity measurement. Trusted computing dependent on hardware root of trust has been produced by industry to secure computing frameworks and billions of end points. Remote attestation provides a facility to attestation the required platforms using platform configuration registers (PCR), and sealed storage is used to encrypt the consumer sensitive data using cryptographic operations. Integrity measurements are used to measure the given computing components in respective register. Here, the authors concentrated on a trusted computing paradigm to enable cloud service providers to solve the potential insider attacks at cloud premises.

Implementation Techniques for Extensible Object Storage Systems

2011 ◽  
pp. 104-127
Author(s):  
Jung-Ho Ahn ◽  
Ha-Joo Song ◽  
Hyoung-Joo Kim
Keyword(s):  
High Performance ◽  
Storage System ◽  
Object Oriented ◽  
Middle Layer ◽  
Database Systems ◽  
Object Oriented Design ◽  
Design And Implementation ◽  
Pointer Swizzling ◽  
Implementation Techniques ◽  
Object Storage

An efficient object manager, a middle layer on top of a storage system, is essential to ensure acceptable performance of object-oriented database systems, since a traditional record-based storage system is too simple to provide object abstraction. In this chapter, we design and implement an extensible object storage system, called Soprano, in an object-oriented fashion which has shown great potential in extensibility and code reusability. Soprano provides a uniform object abstraction and gives us the convenience of persistent programming through many useful persistent classes. Also, Soprano supports efficient object management and pointer swizzling for fast object access. This chapter investigates several aspects of the design and implementation of the extensible object storage system. Our experience shows the feasibility of using an object-oriented design and implementation in building an object storage system that should have both extensibility and high performance.

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