Implementing Support for Pointers to Private Data in a General-Purpose Secure Multi-Party Compiler

Many embedded systems feature processors coupled with a small and fast scratchpad memory. To the difference with caches, allocation of data to scratchpad memory must be handled by software. The major gain is to enhance the predictability of memory accesses latencies. A compile-time dynamic allocation approach enables eviction and placement of data to the scratchpad memory at runtime. Previous dynamic scratchpad memory allocation approaches aimed to reduce average-case program execution time or the energy consumption due to memory accesses. For real-time systems, worst-case execution time is the main metric to optimize. In this paper, we propose a WCET-directed algorithm to dynamically allocate static data and stack data of a program to scratchpad memory. The granularity of placement of memory transfers (e.g. on function, basic block boundaries) is discussed from the perspective of its computation complexity and the quality of allocation.

Frequent dynamic memory allocations (DyMAs) can significantly hinder the scalability of parallel multi-threaded programs. As the number of threads grows, DyMAs can even become the main performance bottleneck. We introduce modern tools and methods for evaluating the impact of DyMAs and present techniques for its reduction, which include scalable heap implementations, small buffer optimization, and memory pooling. Additionally, we provide a survey of state-of-the-art implementations of these techniques and study them experimentally by using a benchmark program, server simulator software, and a real-world high-performance computing application. As a result, we show that relatively small modifications in parallel program's source code or a way of its execution may substantially reduce the runtime overhead associated with the use of dynamic data structures.

Secure Multi-Party Computation: Theory, practice and applications

In a Secure Multiparty Computation (SMC), mutually distrusting parties use cryptographic techniques to cooperatively compute over their private data, in the process each party learns only explicitly revealed outputs. In this paper, we present Wysteria, a high-level programming language for writing SMCs. As with past languages, like Fairplay, Wysteria compiles secure computations to circuits that are executed by an underlying engine. Unlike past work, Wysteria provides support for mixed-mode programs, which combine local, private computations with synchronous SMCs. Wysteria complements a standard feature set with built-in support for secret shares and with wire bundles, a new abstraction that supports generic n-party computations. We have formalized Wysteria, its refinement type system, and its operational semantics. We show that Wysteria programs have an easy-to-understand single-threaded interpretation and prove that this view corresponds to the actual multi-threaded semantics. We also prove type soundness, a property we show has security ramifications, namely that information about one party's data can only be revealed to another via (agreed upon) secure computations. We have implemented Wysteria, and used it to program a variety of interesting SMC protocols from the literature, as well as several new ones. We find that Wysteria's performance is competitive with prior approaches while making programming far easier, and more trustworthy.

This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to collaboratively train models without sharing their local datasets. Aggregation is a pivotal aspect of FL, as it integrates model updates from diverse clients into a unified global model while addressing critical challenges such as data heterogeneity, scalability, and privacy preservation. The study categorizes aggregation strategies into three primary approaches: data-centric, model-centric, and secure aggregation, each tailored to address distinct problems in FL systems. Data-centric strategies focus on addressing non-independent and identically distributed (non-IID) data across clients, ensuring that model updates account for imbalances in data distributions. Model-centric strategies optimize the aggregation of model parameters, emphasizing techniques such as weighted averaging and model distillation to improve model performance across clients with diverse data characteristics. Secure aggregation techniques aim to enhance privacy and robustness, protecting client data from potential adversarial threats through encryption-based methods like secure multi-party computation (SMPC) and Byzantine-resilient techniques. The analysis delves into the advantages and limitations of these aggregation strategies, particularly their role in tackling challenges like non-IID data, communication efficiency, and resistance to adversarial attacks. Furthermore, the article identifies existing research gaps in FL, including the need for more scalable and robust aggregation methods capable of reducing communication costs, enhancing privacy guarantees, and improving performance in highly heterogeneous environments. These insights provide a roadmap for future research aimed at advancing aggregation strategies in FL to improve model accuracy, security, and efficiency in real-world applications.

Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardware-protection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.

Dynamic memory allocators are critical components of modern systems, and developers strive to find a balance between their performance and their security. Unfortunately, vulnerable allocators are routinely abused as building blocks in complex exploitation chains. Most of the research regarding memory allocators focuses on popular and standardized heap libraries, generally used by high-end devices such as desktop systems and servers. However, dynamic memory allocators are also extensively used in embedded systems but they have not received much scrutiny from the security community.In embedded systems, a raw firmware image is often the only available piece of information, and finding heap vulnerabilities is a manual and tedious process. First of all, recognizing a memory allocator library among thousands of stripped firmware functions can quickly become a daunting task. Moreover, emulating firmware functions to test for heap vulnerabilities comes with its own set of challenges, related, but not limited, to the re-hosting problem.To fill this gap, in this paper we present HEAPSTER, a system that automatically identifies the heap library used by a monolithic firmware image, and tests its security with symbolic execution and bounded model checking. We evaluate HEAPSTER on a dataset of 20 synthetic monolithic firmware images — used as ground truth for our analyses — and also on a dataset of 799 monolithic firmware images collected in the wild and used in real-world devices. Across these datasets, our tool identified 11 different heap management library (HML) families containing a total of 48 different variations. The security testing performed by HEAPSTER found that all the identified variants are vulnerable to at least one critical heap vulnerability. The results presented in this paper show a clear pattern of poor security standards, and raise some concerns over the security of dynamic memory allocators employed by IoT devices.

Randomization is used in computer security as a tool to introduce unpredictability into the software infrastructure. In this paper, we study the use of randomization to achieve the secrecy and integrity guarantees for local memory. We follow the approach set out by Abadi and Plot kin. We consider the execution of an idealized language in two environments. In the strict environment, opponents cannot access local variables of the user program. In the lax environment, opponents may attempt to guess allocated memory locations and thus, with small probability, gain access the local memory of the user program. We model these environments using two novel calculi: lambda-mu-hashref and lambda-mu-proberef. Our contribution to the Abadi-Plot kin program is to enrich the programming language with dynamic memory allocation, first class and higher order references and call/cc-style control. On the one hand, these enhancements allow us to directly model a larger class of system hardening principles. On the other hand, the class of opponents is also enhanced since our enriched language permits natural and direct encoding of attacks that alter the control flow of programs. Our main technical result is a fully abstract translation (up to probability) of lambda-mu-hashref into lambda-mu-proberef. Thus, in the presence of randomized layouts, the opponent gains no new power from being able to guess local references of the user program. Our numerical bounds are similar to those of Abadi and Plot kin, thus, the extra programming language features do not cause a concomitant increase in the resources required for protection via randomization.

Some programming languages and computer systems use dynamic memory allocation with garbage collection. It would be useful to understand how the utilization of memory depends on the stochastic parameters describing the size and life distributions of the cells. We consider a class of dynamic storage allocation systems which use a first-fit strategy to allocate cells and perform noncompacting garbage collections to recover free memory space when memory becomes fully occupied. A formula is derived for the expected number of holes (available cells) in memory immediately following a garbage collection which specializes to an analogue of Knuth's 'Fifty Percent' rule for nongarbage-collection systems. Simulations confirm the rule for exponentially distributed cell lifetimes. Other lifetime distributions are discussed. The memory-size requirements for noncompacting garbage collection are also analyzed.

Recently there has been increasing interest in supporting execution of Java Virtual Machine (JVM) applications on accelerator architectures, such as GPUs. Unfortunately, there is a large gap between the features of the JVM and those commonly supported by accelerators. Examples of important JVM features include exceptions, dynamic memory allocation, use of arbitrary composite objects, file I/O, and more. Recent work from our research group tackled the first feature in that list, JVM exception semantics[14]. This paper continues along that path by enabling the acceleration of JVM parallel regions that include object references and dynamic memory allocation.

The high take-up rate of artificial intelligence (AI) by different sectors has triggered critical interest in data security, privacy, and ethics compliance. This work discusses the design and process blueprint for the establishment of AI pipelines that comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and different sectoral standards. The aim is to offer a sound methodology to incorporate compliance checks at every stage of the AI lifecycle, from data collection to deployment. Our method focuses on incorporating privacy-preserving methods, secure data engineering principles, and auditability. Through a comparative analysis of AI development processes and regulatory requirements, we identify friction points and suggest technical as well as organizational solutions. The paper also analyzes case studies to test the proposed framework and addresses its implication for AI governance and ethical AI deployment. The research highlights the need for cross-disciplinary cooperation between legal professionals, data scientists, and organizational stakeholders to provide end-to-end compliance. We propose certain tools and technologies, including federated learning and secure multiparty computation, that enable the enforcement of regulatory requirements without undermining model effectiveness. This paper makes a contribution to current discussion by offering a guidebook to action for businesses to enact ethical AI while preserving competitive edge in a highly regulated digital environment. Keywords- AI pipeline, GDPR, HIPAA, data privacy, compliance, ethical AI, data governance, security standards, machine learning, privacy-preserving computation

One of the greatest challenges in C/C++-based design methodology is to efficiently map C/C++ models into hardware. Many of the networking and multimedia applications implemented in hardware or mixed hardware/software systems are making use of complex data structures stored in one or multiple memories. As a result, many of the C/C++ features which were originally designed for software applications are now making their way into hardware. Such features include dynamic memory allocation and pointers used to manage data. We present a solution for efficiently mapping arbitrary C code with pointers and malloc/free into hardware. Our solution fits current memory management methodologies. It consists of instantiating a hardware allocator tailored to an application and a memory architecture. Our work also supports the resolution of pointers without restriction on the data structures. An implementation using the SUIF framework is presented, followed by some case studies such as the realization of a video filter.

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

With the advent of virtualization and cloud computing, virtualized systems can be found from small companies to service providers and big data centers. All of them use this technology because of the many benefits it has to offer, such as a greener ICT, cost reduction, improved profitability, uptime, flexibility in management, maintenance, disaster recovery, provisioning and more. The main reason for all of these benefits is server consolidation which can be even further improved through dynamic resource allocation techniques. Out of the resources to be allocated, memory is one of the most difficult and requires proper planning, good predictions and proactivity. Many attempts have been made to approach this problem, but most of them are using traditional statistical mathematical methods. In this paper, the application of discrete Bayesian networks is evaluated, to offer probabilistic predictions on system utilization with focus on memory. The tool Bayllocator is built to provide proactive dynamic memory allocation based on the Bayesian predictions, for a set of virtual machines running in a single hypervisor. The results show that Bayesian networks are capable of providing good predictions for system load with proper tuning, and increase performance and consolidation of a single hypervisor. The modularity of the tool gives a great freedom for experimentation and even results to deal with the reactivity of the system can be provided. A survey of the current state-of-the-art in dynamic memory allocation for virtual machines is included in order to provide an overview.

This textbook provides in-depth coverage of the fundamentals of the C and C++ programming languages and the object-oriented programming paradigm. It follows an example-driven approach to facilitate understanding of theoretical concepts. Essential concepts, including functions, arrays, pointers and inheritance, are explained, while complex topics, such as dynamic memory allocation, object slicing, vtables, and upcasting and downcasting, are examined in detail. Concepts are explained with the help of line diagrams, student-teacher conversations and flow charts, while other useful features, such as quiz questions and points to remember, are included. Solved examples, review questions and useful case studies are interspersed throughout the text, and explanations of the logic used to implement particular functionality is also provided. This book will be useful for undergraduate students of computer science and engineering, and information technology.