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State of the Sandbox: Investigating macOS application security 18

State of the Sandbox: Investigating macOS Application Securi...

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2019 Workshop on privacy in the Electronic Society

作者： Blochberger, Maximilian Rieck, Jakob Burkert, Christian Mueller, Tobias Federrath, Hannes Univ Hamburg Hamburg Germany

ISBN: (纸本)9781450368308

Sandboxing is a way to deliberately restrict applications accessing resources that they do not need to function properly. Sandboxing is intended to limit the effect of potential exploits and to mitigate overreach to personal data. Since June 1, 2012, sandboxing is a mandatory requirement for apps distributed through the Mac App Store (MAS). In addition, Apple has made it easier for developers to specify sandbox entitlements - capabilities that allow the app to access certain resources. However, sandboxing is still optional for macOS apps distributed outside Apple's official app store. This paper provides two contributions. First, the sandbox mechanism of macOS is analyzed and a critical sandbox-bypass is identified. Second, the general adoption of the sandbox mechanism, as well as app-specific sandbox configurations are evaluated. For that purpose all 8366 free apps of the MAS, making 25 % of all apps available on the MAS, as well as 4672 apps retrieved from MacUpdate (MU), a third-party app store, were analyzed dynamically. The dataset is over eight times larger than the second biggest study of macOS apps. It is shown that more than 94 % of apps on the MAS are sandboxed. However, more than 89 % of apps distributed through MU do not make use of sandboxing, putting users' data at risk.

关键词： sandboxing capabilities entitlements macOS

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Extracting Secrets from Encrypted Virtual Machines 19

Extracting Secrets from Encrypted Virtual Machines

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9th acm conference on data and application security and privacy (CODASPY)

作者： Morbitzer, Mathias Huber, Manuel Horsch, Julian Fraunhofer AISEC Garching Germany

ISBN: (纸本)9781450360999

AMD SEV is a hardware extension for main memory encryption on multi-tenant systems. SEV uses an on-chip coprocessor, the AMD Secure Processor, to transparently encrypt virtual machine memory with individual, ephemeral keys never leaving the coprocessor. The goal is to protect the confidentiality of the tenants' memory from a malicious or compromised hypervisor and from memory attacks, for instance via cold boot or DMA. The SEVered attack has shown that it is nevertheless possible for a hypervisor to extract memory in plaintext from SEV-encrypted virtual machines without access to their encryption keys. However, the encryption impedes traditional virtual machine introspection techniques from locating secrets in memory prior to extraction. This can require the extraction of large amounts of memory to retrieve specific secrets and thus result in a time-consuming, obvious attack. We present an approach that allows a malicious hypervisor quick identification and theft of secrets, such as TLS, SSH or FDE keys, from encrypted virtual machines on current SEV hardware. We first observe activities of a virtual machine from within the hypervisor in order to infer the memory regions most likely to contain the secrets. Then, we systematically extract those memory regions and analyze their contents on-the-fly. This allows for the efficient retrieval of targeted secrets, strongly increasing the chances of a fast, robust and stealthy theft.

关键词： AMD SEV virtual machine encryption virtual machine introspection memory extraction data confidentiality

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Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment 19

Neural Network Inversion in Adversarial Setting via Backgrou...

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acm SIGSAC conference on Computer and Communications security (CCS)

作者： Yang, Ziqi Zhang, Jiyi Chang, Ee-Chien Liang, Zhenkai Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781450367479

The wide application of deep learning technique has raised new security concerns about the training data and test data. In this work, we investigate the model inversion problem under adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion model in the adversarial settings. First, we leverage the adversary's background knowledge to compose an auxiliary set to train the inversion model, which does not require access to the original training data. Second, we design a truncation-based technique to align the inversion model to enable effective inversion of the target model from partial predictions that the adversary obtains on victim user's data. We systematically evaluate our approach in various machine learning tasks and model architectures on multiple image datasets. We also confirm our results on Amazon Rekognition, a commercial prediction API that offers "machine learning as a service". We show that even with partial knowledge about the black-box model's training data, and with only partial prediction values, our inversion approach is still able to perform accurate inversion of the target model, and outperform previous approaches.

关键词： neural networks deep learning model inversion security privacy

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A Practical Intel SGX Setting for Linux Containers in the Cloud 19

A Practical Intel SGX Setting for Linux Containers in the Cl...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Tian, Dave (Jing) Choi, Joseph, I Hernandez, Grant Traynor, Patrick Butler, Kevin R. B. Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9781450360999

With close to native performance, Linux containers are becoming the de facto platform for cloud computing. While various solutions have been proposed to secure applications and containers in the cloud environment by leveraging Intel SGX, most cloud operators do not yet offer SGX as a service. This is likely due to a number of security, scalability, and usability concerns coming from both cloud providers and users. Cloud operators worry about the security guarantees of unofficial SDKs, limited support for remote attestation within containers, limited physical memory for the Enclave Page Cache (EPC) making it difficult to support hundreds of enclaves, and potential DoS attacks against EPC by malicious users. Meanwhile, end users need to worry about careful program partitioning to reduce the TCB and adapting legacy applications to use SGX. We note that most of these concerns are the result of an incomplete infrastructure, from the OS to the application layer. We address these concerns with lxcsgx, which allows SGX applications to run inside containers while also: enabling SGX remote attestation for containerized applications, enforcing EPC memory usage control on a per-container basis, providing a general software TPM using SGX to augment legacy applications, and supporting partitioning with a GCC plugin. We then retrofit Nginx/OpenSSL and Memcached using the software TPM and SGX partitioning to defend against known and potential attacks. Thanks to the small EPC footprint of each enclave, we are able to run up to 100 containerized Memcached instances without EPC swapping. Our evaluation shows the overhead introduced by lxcsgx is less than 6.9% for simple SGX applications, 9.5% for Nginx/OpenSSL, and 20.9% for containerized Memcached.

关键词： Cloud Containers security SGX

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AsiaCCS 2019 - proceedings of the 2019 acm Asia conference on Computer and Communications security

AsiaCCS 2019 - Proceedings of the 2019 ACM Asia Conference o...

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2019 acm Asia conference on Computer and Communications security, AsiaCCS 2019

ISBN: (纸本)9781450367523

The proceedings contain 68 papers. The topics discussed include: control-flow carrying code;DeClassifier: class-inheritance inference engine for optimized C++ binaries;GraphSE2: an encrypted graph database for privacy-preserving social search;identity-based broadcast encryption with outsourced partial decryption for hybrid security models in edge computing;unveiling systematic biases in decisional processes: an application to discrimination discovery;ObliDC: an SGX-based oblivious distributed computing framework with formal proof;running language interpreters inside SGX: a lightweight, legacy-compatible script code hardening approach;multi-writer searchable encryption: an LWE-based realization and implementation;delegatable order-revealing encryption;and flexibly and securely shape your data disclosed to others.

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Unveiling Systematic Biases in Decisional Processes An application to Discrimination Discovery 19

Unveiling Systematic Biases in Decisional Processes An Appli...

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acm Asia conference on Computer and Communications security (Asia CCS)

作者： Genga, Laura Allodi, Luca Zannone, Nicola Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781450367523

Decisional processes are at the basis of several security and privacy applications. However, they are often not transparent and can be affected by human or algorithmic biases that may lead to systematically misleading or unfair outcomes. To unveil these biases, one has to identify which information was used to make the decision and to quantify to what extent such information has influenced the process outcome. Two classes of techniques are widely used to determine possible correlation between variables within decisional processes from observational data: (i) econometric techniques, in particular regression analysis, and (ii) knowledge discovery techniques, in particular association rules mining. However, these techniques, taken individually, have intrinsic drawbacks that limit their applicability. In this work, we propose an approach for unveiling biases in decisional processes, which leverages association rule mining for systematic hypothesis generation and regression analysis for model selection and recommendation extraction. We demonstrate the proposed approach in the context of discrimination detection, showing that not only it provides 'statistically significant' evidence of discrimination but it also allows for a more efficient operationalization of the recommendations extracted, upon which the decision maker can operate.

关键词： Decisional Process Econometrics Association Rule Mining

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MimosaFTL: Adding Secure and Practical Ransomware Defense Strategy to Flash Translation Layer 19

MimosaFTL: Adding Secure and Practical Ransomware Defense St...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Wang, Peiying Jia, Shijie Chen, Bo Xia, Luning Liu, Peng Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA Penn State Univ University Pk PA 16802 USA Chinese Acad Sci Data Assurance & Commun Secur Res Ctr Beijing Peoples R China

ISBN: (纸本)9781450360999

Ransomware attacks have become prevalent nowadays due to sudden flourish of cryptocurrencies. Most existing defense strategies for ransomware, however, are vulnerable to privileged ransomware who can compromise the operating system and hence any backup data stored locally. The out-of-place-update and the isolation nature of flash memory storage, for the first time, makes it possible to design a defense strategy which is secure against the privileged ransomware. In this work, we propose MimosaFTL, a secure and practical ransomware defense strategy for mobile computing devices equipped with flash memory as external storage. MimosaFTL is secure against the privileged malware by taking advantage of unique characteristics of flash storage. In addition, it is more practical (compared to prior work) for real-world deployments by: 1) incorporating a fine-grained detection scheme which can detect presence of ransomware accurately;and 2) allowing the victim to efficiently restore the infected external storage to the exact point when the malware starts to perform corruption. Experimental evaluation shows that, MimosaFTL can mitigate ransomware attacks effectively with a small negative impact on both I/O performance and lifetime of flash storage.

关键词： Ransomware Mobile devices Flash translation layer Access patterns Ransomware detection Binary search

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A general framework for privacy-preserving computation on cloud environments 19

A general framework for privacy-preserving computation on cl...

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12th IEEE/acm International conference on Utility and Cloud Computing, UCC 2019

作者： Basilakis, Jim Javadi, Bahman School of Computing Eng. and Math. Western Sydney University Sydney Australia

ISBN: (纸本)9781450368940

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that can potentially assure secure processing of sensitive data by third-party cloud vendors. It relies on the fact that computations can occur on encrypted data without the need for decryption, although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. This paper examines a proposed technology platform, known as the Homomorphic Encryption Bus (HEB), that leverages HE with data obfuscation methods over a minimal network interaction model, allowing a uniform, flexible and general approach to cloud-based privacy-preserving system integration. The platform is uniquely designed to overcome barriers limiting the mainstream application of existing Fully Homomorphic Encryption (FHE) schemes in the cloud. A client-server interaction model involving ciphertext decryption on the client end is necessary to achieve resetting of 'noisy' ciphertexts in place of a much more inefficient (server only) recryption procedure. data perturbation techniques are used to obfuscate intermediate data decrypted on the client-side of ciphertext interactions, in a way that is unintelligible to the client. In addition to efficient noise resetting, interactions involving data perturbations also achieve plaintext (binary to integer-based and vice versa) message space swapping, and conversion of accumulated integerbased encodings to a reduced embedded binary form. There appears to be little existing literature that examines these techniques as a means of broadening HE processing capabilities and practical application over the cloud. Interaction performance is examined in terms of timing and multiplicative circuit depth costs, through a simple equation evaluation and against standard recryption. © 2019 Association for Computing Machinery.

关键词： data obfuscation

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dataBlinder: A distributed data protection middleware supporting search and computation on encrypted data 19

DataBlinder: A distributed data protection middleware suppor...

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20th International Middleware conference Industrial Track (Middleware Industry)

作者： Heydari, Emad Lagaisse, Bert Joosen, Wouter Aly, Abdelrahaman Brackx, Michael Katholieke Univ Leuven Imec DistriNet Leuven Belgium Katholieke Univ Leuven Imec COSIC Leuven Belgium UnifiedPost La Hulpe Belgium

ISBN: (纸本)9781450370417

Business application owners want to outsource data storage, including sensitive data, to the public cloud for economical reasons. This is often challenging since these businesses are and remain responsible for regulatory compliance and data protection, even though cloud providers may do their best to offer (data) protection. Meanwhile, data protection techniques evolve and get better because of continuous research and improvement of advanced encryption. Numerous cryptographic tactics have been proposed, e.g., searchable symmetric encryption (SSE) and homomorphic encryption (HE), that support search and aggregation functions on encrypted data. Each of these tactics has a trade-off between security, performance and functionality, but there is no one-size-fits-all solution. For the application developer, the underpinning concepts of these tactics are complex to comprehend, complex to integrate in a distributed application, and prone to implementation mistakes. In this paper we present dataBlinder, a distributed data access middleware that provides crypto agility by means of configurable fine-grained data protection at the application level. dataBlinder supports adaptive runtime selection of data protection tactics, and offers a plugin architecture for such tactics based on a key abstraction model for protection level, performance and supported query functionality. We have developed this middleware in close collaboration with businesses that face these challenges and offer cloud-based applications in e-finance, and e-health, by implementing and integrating state-of-the-art cryptographic schemes to dataBlinder. This paper illustrates the case of medical data protection with FHIR-compliant [30] medical data.

关键词： security and privacy middleware data protection

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Ramparts: A Programmer-Friendly System for Building Homomorphic Encryption applications 7

Ramparts: A Programmer-Friendly System for Building Homomorp...

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7th acm Workshop on Encrypted Computing and Applied Homomorphic Cryptography (WAHC)

作者： Archer, David W. Trilla, Jose Manuel Calderon Dagit, Jason Malozemoff, Alex J. Polyakov, Yuriy Rohloff, Kurt Ryan, Gerard Galois Inc Portland OR 97204 USA New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9781450368292

Homomorphic Encryption (HE) is an emerging technology that enables computing on data while the data is encrypted. A major challenge with homomorphic encryption is that it takes extensive expert knowledge to design meaningful and useful programs that are constructed from atomic HE operations. We present Ramparts to address this challenge. Ramparts provides an environment for developing HE applications in Julia, a high-level language, the same way as "cleartext" applications are typically written in Julia. Ramparts makes the following three contributions. First, we use symbolic execution to automate the construction of an optimized computation circuit where both the circuit size and multiplicative depth are chosen by the compiler. Second, Ramparts automatically selects the HE parameters for the generated circuit, which is typically done manually by an HE expert. Third, Ramparts automatically selects the plaintext encoding for input values, and performs input and output data transformations. These three operations are not easily performed by programmers who are not HE experts. Thus, Ramparts makes HE more widely available and usable by the the population of programmers. We compare our approach with Cingulata, the only previously known system that automatically generates circuits for HE computations. The HE circuits generated by Ramparts are significantly more efficient than the circuits compiled by Cingulata. For instance, our runtimes for key generation/circuit compilation and all online operations are more than one order of magnitude lower for a sample image processing application used for performance evaluation in our study.

关键词： Homomorphic encryption Automatic arithmetic circuit generation Lattice-based cryptography Usable security and privacy

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