Private resource allocators and their applications

When a software implementation of a security system is considered, side channel attacks are on the spot threat. Side-channel attacks, considered in this work, are a class of physical attacks in which an adversary tries to exploit physical information leakages such as timing information, power consumption, or electromagnetic radiation to estimate security system parameters during performing security algorithms. New system architecture features, such as larger cache sizes and multicore processors, have increased the prevalence of side channels, in addition to, the availability of measurement apparatuses to an attacker. Thus, software developers must be aware of the potential for side-channel attacks and plan appropriately. In this paper, we propose a software implementation of a hashing method based on SHA3-512 hashing algorithm that can counteract the side-channel attacks. To achieve our goal, we use three techniques, first, we shuffle the dataset, salt, and process selector arrays using "Fisher Yates" algorithm. Second, we use volatile memory objects to hold critical data. Finally, we apply the thread locking technique where at most one thread can access the critical objects at a time. Experimental results show that our proposed hashing method is more secure than other related methods. Although there is a trade-off between hashing security and hashing processing time, the processing time of the proposed hashing method is still acceptable.

A longstanding problem in computer privacy is that of data anonymization. One common approach is to present a query interface to analysts, and anonymize on a query-by-query basis. In practice, this approach often uses a standard database back end, and presents the query semantics of the database to the analyst. This paper presents a class of novel side-channel attacks that work against any query-based anonymization system that uses a standard database back end. The attacks exploit the implicit conditional logic of database runtime optimizations. They manipulate this logic to trigger timing and exception-throwing side-channels based on the contents of the data. We demonstrate the attacks on the implementation of the CHORUS Differential Privacy system released by Uber as an open source project. We obtain perfect reconstruction of millions of data values even with a Differential Privacy budget smaller than epsilon = 1.0 and no prior knowledge. The paper also presents the design of a general defense to the runtime-optimization attacks, and a concrete implementation of the defense in the latest version of Diffix. The defense works without modifications to the back end database, and operates by modifying SQL to eliminate the runtime optimization or disable the side-channels. In addition, two other attacks that exploit specific flaws in Diffix and CHORUS are reported. These have been fixed in the respective implementations.

Stealing of sensitive information from apps is always considered to be one of the most critical threats to Android security. Recent studies show that this can happen even to the apps without explicit implementation flaws, through exploiting some design weaknesses of the operating system, e.g., Shared communication channels such as Bluetooth, and side channels such as memory and network-data usages. In all these attacks, a malicious app needs to run side-by-side with the target app (the victim) to collect its runtime information. Examples include recording phone conversations from the phone app, gathering WebMD's data usages to infer the disease condition the user looks at, etc. This runtime-information-gathering (RIG) threat is realistic and serious, as demonstrated by prior research and our new findings, which reveal that the malware monitoring popular Android-based home security systems can figure out when the house is empty and the user is not looking at surveillance cameras, and even turn off the alarm delivered to her phone. To defend against this new category of attacks, we propose a novel technique that changes neither the operating system nor the target apps, and provides immediate protection as soon as an ordinary app (with only normal and dangerous permissions) is installed. This new approach, called App Guardian, thwarts a malicious app's runtime monitoring attempt by pausing all suspicious background processes when the target app (called principal) is running in the foreground, and resuming them after the app stops and its runtime environment is cleaned up. Our technique leverages a unique feature of Android, on which third-party apps running in the background are often considered to be disposable and can be stopped anytime with only a minor performance and utility implication. We further limit such an impact by only focusing on a small set of suspicious background apps, which are identified by their behaviors inferred from their side channels (e.g., Thread names, CPU scheduling and kernel time). App Guardian is also carefully designed to choose the right moments to start and end the protection procedure, and effectively protect itself against malicious apps. Our experimental studies show that this new technique defeated all known RIG attacks, with small impacts on the utility of legitimate apps and the performance of the OS. Most importantly, the idea underlying our approach, including app-level protection, side-channel based defense and lightweight response, not only significantly raises the bar for the RIG attacks and the research on this subject but can also inspire the follow-up effort on new detection systems practically deployable in the fragmented Android ecosystem.

The capability that a server can hide its location while offering various kinds of services to its clients is called hidden services or location-hiding. Almost previous low-latency anonymous communication systems such as Tor, MorphMix, etc. that can be used to implement hidden services are vulnerable against end-to-end traffic analysis attack. In this paper, we introduce a novel architecture for implementing hidden services which is robust against end-to-end traffic analysis attack. Moreover, our scheme is more robust against various traffic analysis attacks than previous low-latency anonymous communication architectures.KeywordsOverlay NetworkMalicious NodeClockwise DirectionResponse PacketRendezvous PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Intel Software Guard Extension (SGX) protects the confidentiality and integrity of an unprivileged program running inside a secure enclave from a privileged attacker who has full control of the entire operating system (OS). Program execution inside this enclave is therefore referred to as shielded. Unfortunately, shielded execution does not protect programs from side-channel attacks by a privileged attacker. For instance, it has been shown that by changing page table entries of memory pages used by shielded execution, a malicious OS kernel could observe memory page accesses from the execution and hence infer a wide range of sensitive information about it. In fact, this page-fault side channel is only an instance of a category of side-channel attacks, here called privileged side-channel attacks, in which privileged attackers frequently preempt the shielded execution to obtain fine-grained side-channel observations. In this paper, we present Deja Vu, a software framework that enables a shielded execution to detect such privileged side-channel attacks. Specifically, we build into shielded execution the ability to check program execution time at the granularity of paths in its control-flow graph. To provide a trustworthy source of time measurement, Deja Vu implements a novel software reference clock that is protected by Intel Transactional Synchronization Extensions (TSX), a hardware implementation of transactional memory. Evaluations show that Deja Vu effectively detects side-channel attacks against shielded execution and against the reference clock itself.

The CPU architecture landscape is constantly evolving to optimize performance. However, this has inadvertently exposed vulnerabilities such as microarchitectural traces that can be exploited in side-channel attacks. The Branch Prediction Unit (BPU) plays a critical role in improving processor performance, but also introduces vulnerabilities to microarchitectural side-channel attacks. Despite ongoing efforts to develop defensive techniques, the continued emergence of new attack methods underscores the need for comprehensive analysis. This article aims to address this research gap by conducting a thorough investigation of BPU-based side-channel attacks. This survey presents a novel taxonomy for the systematic classification of BPU-based side-channel attacks and defenses. The attacks and defenses are categorized based on three components: manipulated unit, core technique, and disclosure method. The analysis provides a structured evaluation of the effectiveness of defense techniques against each attack technique. This study not only enhances the understanding of BPU exploitation, but also provides valuable insights for software developers and CPU designers to help them protect against evolving side-channel threats.

Side-channel attacks are a prominent threat to the security of cryptographic implementations. Differently from the traditional black-box attacks, which exploit the inputs and outputs of cryptographic schemes, side-channel attacks partially access the inner working of the scheme as well, by observing the physical leakage emitted by the device executing cryptographic algorithms. A notable example is the class of power-analysis attacks, which exploits the power consumption of the underlying device to recover the secret keys of the implemented cryptosystem. Since their first presentation in the late 1990s, the problem of securing cryptographic systems in the presence of side-channel leakages received significant attention by the cryptographic community. In particular, the theory community has concentrated its research efforts on formally modeling the side-channel leakages and on designing cryptographic schemes \textit{provably} secure in such leakage models. However, conceiving a formal model that realistically captures possible physical leakages and constructing primitives sufficiently efficient to be deployed in practice are still challenging research objectives. This research area is usually referred to as leakage-resilient cryptography. In this dissertation, we examine three main research directions. The first one focuses on the technique of masking, which is the most popular approach to counteract power-analysis attacks. In this context, our work analyzes possible methods to improve the efficiency of masked implementations, with the goal of overcoming the decrease in performance, which is a common drawback of masking. In particular, we provide new and more efficient algorithms for the computation of basic operations in two kinds of masking schemes: the Boolean masking and the Inner Product masking. The second research direction of this work addresses the problem of formalizing security requirements for hardware implementations. While provable security of software-oriented masking has been studied since the origin of leakage resilient cryptography, the more complex task of formally proving the security of hardware-oriented masking is receiving attention by the community only recently. We discuss the different challenges of this field, and we introduce a new theoretical model, the robust probing model, capturing the conditions required for securely implementing complex algorithms in practice, providing a guide to practical implementers. Finally, the last part of this work is concerned with the protection of algorithms employed in lattice-based constructions, which are studied in the field of post-quantum cryptography. While several works investigate the application of masking schemes to standard cryptosystems, for most post-quantum schemes this is still an ongoing research. We contribute to this research area, by applying the masking countermeasure to a binomial sampler which is at the base of many lattice-based cryptosystems.

Microelectronic crypto devices contain intellectual property like secret data to be protected against side channel attack. Scan chain based attacks come under the category of side channel attack where the hackers attack a scan path through observing and comparing the relationship between intermediate hamming distances values for different test vector patterns. Hence our novel hamming model should overcome the scan based attack and should not give any correlation relationship in hamming distance by providing the similar intermediate values for all test vector patterns which are obtained through an optimal way of inserting Optimal Scan Flip Flop (OSFF) randomly to the scan path chain. Implementation of our proposed integrated circuits is written in Verilog and synthesised with XILINX Spartan III FPGA. The report is compared with Robust Scan Flip Flop (RFSS) hamming model to estimate the overhead of component minimized in OSFF.

Security in embedded systems is considered to be more important and needs to be a diagnosis for every minute. Also with the advent of the Internet of Things (IoT), security in the embedded system has reached its new peak of dimension. A Mathematically secure algorithm was formulated and runs on the cryptographic chips which are embedded in the systems, but secret keys can be at risk and even information can be retrieved by the prominent side-channel attacks. Fixed encryption keys, non-intelligent detection of side-channel attacks are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on the integration of powerful machine learning algorithms by retrieving the secret key information with countermeasures methodology using the chaotic logistic maps and includes the following contributions: (a) Preparation of Data Sets from the Power consumption traces captured from ARTIX-7 FPGA boards while running the Elliptical Curve Cryptography(ECC) on it (b) Implementation of High Speed and High Accurate Single feed-forward learning machines for the detection and classification of side-channel attacks (c) Design of Chaotic Countermeasures using 3-Dlogistic maps for attacked bits. The test_bed has been developed using the integration of FPGA along with Cortex-A57 architectures for experimentation of the proposed work and various evaluation parameters such as Accuracy, F-calls, Precision rates, sensitivity, and correlation co-efficient, entropy were calculated and analyzed. Moreover, the parameters of the proposed system which has been analyzed prove to outperform the other existing algorithms in terms of performance and detection.

In this paper, we study the problem of traffic analysis attacks in wireless body area sensor networks. When these networks are used in health-care for remote patient monitoring, traffic analysis can reveal the type of medical sensors mounted on the patient, and this information may be used to infer the patient's health problems. We show that simple signal processing methods can be used effectively for performing traffic analysis attacks and identifying the sensor types in a rather weak adversary model. We then investigate possible traffic obfuscation mechanisms aiming at hiding the regular patterns in the observable wireless traffic. Among the investigated countermeasures, traffic shaping, a mechanism that introduces carefully chosen delays for message transmissions, appears to be the best choice, as it achieves close to optimal protection and incurs no overhead.

The Tor anonymity network is one of the most popular and widely used tools to protect the privacy of online users. Tor provides defenses against multiple adversarial activities aiming to identify or trace the users. Traffic analysis is a very strong tool that can be used for internet surveillance. Traffic analysis attacks against Tor's anonymity network has been known as an open question in research. Moreover, the low-latency feature Tor tries to provide to its users imposes limitations in defending against traffic analysis attacks. In our study, we examine traffic analysis attacks from the perspective of the adopted adversary model and how much it fits within Tor's threat model. The purpose of this study is to evaluate how practical these attacks are on real-time Tor network.

Micro-architectural side-channel attacks are major threats to the most mathematically sophisticated encryption algorithms. In spite of the fact that there exist several defense techniques, the overhead of implementing the countermeasures remains a matter of concern. A promising strategy is to develop online detection and prevention methods for these attacks. Though some recent studies have devised online prevention mechanisms for some categories of these attacks, still other classes remain undetected. Moreover, to detect these side-channel attacks with minimal False Positives is a challenging effort because of the similarity of their behavior with computationally intensive applications. This article presents a generalized machine learning--based multi-layer detection technique that targets these micro-architectural side-channel attacks, while not restricting its attention only on a single category of attacks. The proposed mechanism gathers low-level system information by profiling performance counter events using Linux perf tool and then applies machine learning techniques to analyze the data. A novel approach using time-series analysis of the data is implemented to find out the correlation of the execution trace of the attack process with the secret key of encryption, which helps in dealing with False-Positives and unknown attacks. This article also provides a detailed theoretical analysis of the detection mechanism of the proposed model along with its security analysis. The experimental results show that the proposed method is superior to the state-of-the-art reported techniques with high detection accuracy, low False Positives, and low implementation overhead while being able to detect before the completion of the attack.

The security of a cryptosystem is often compromised, not from a theoretical point of view, but by the leakage caused by the physical implementation of the cryptographic algorithm. A new class of attacks, called physical attacks, has shown the capability to exploit the unintentional physical behaviors from the cryptographic device, which usually provide enough information to recover the secret keys. Dierent methods have been proposed for conducting the attacks. Two of the main focus of physical attacks are side-channel attacks and fault attacks.

SGX-LEGO: Fine-grained SGX controlled-channel attack and its countermeasure

Side-channel attacks are a class of attacks where an attacker deduces the internal state of a device by observing information that leaks during the normal functioning of the device. In this chapter we describe side-channel analysis and its relevance to fault attacks. Side-channel analysis is typically used to extract information about cryptographic keys. However, we will be concentrating on how it can be used as a means to identify target operations and as a trigger mechanism for fault attacks.