Large Language Models (LLMs) are increasingly used in high-stakes domains like law and research, yet their inconsistencies and response instability raise concerns about trustworthiness.This study evaluates six leading LLMs-GPT-3.5, GPT-4, Claude, Gemini, Mistral, and LLaMA 2-on rationality, stability, and ethical fairness through reasoning tests, legal challenges, and bias-sensitive scenarios.Results reveal significant inconsistencies, highlighting trade-offs between model scale, architecture, and logical coherence.These findings underscore the risks of deploying LLMs in legal and policy settings, emphasizing the need for AI systems that prioritize transparency, fairness, and ethical robustness.

Zero-Knowledge Proof (ZKP) cryptographic algorithms have garnered significant attention for their ability to enhance privacy. However, the practical deployment of these algorithms remains challenging because they demand extremely high computational effort and handle huge volumes of data, especially in the Number Theoretic Transform (NTT) step. In this work, we propose an HBM-aware dataflow that employs sub-tiling and row-shuffling techniques to overcome the nonuniform stride access problem and to maximize HBM bandwidth utilization. We also design the NTT accelerator to use minimal FPGA resources. In particular, we explore diverse design options for the 256-bit modular multiplier and adopt an efficient design that optimizes resource usage and performance. Experimental results demonstrate that the proposed accelerator achieves lower latency and enhanced resource utilization compared to state-of-the-art FPGA-based designs.

In this era, significant transformations in industries and tool utilization are driven by AI/Large Language Models (LLMs) and advancements in Machine Learning. There’s a growing emphasis on MLOps for managing and deploying these AI models, along with a focus on distributed inferences. Concurrently, the imperative for secure on-chain computation is escalating. Our paper introduces an innovative framework that integrates blockchain technology, particularly the Cosmos SDK, to facilitate distributed AI inferences on edge devices. This system, built on WebAssembly (WASM), enables interchain communication and deployment of WASM modules executing AI inferences across multiple blockchain nodes. We critically assess this system’s safety, scalability, and model security, with a special focus on its portability and engine-model agnostic deployment on edge devices.

Large language models (LLMs) have showcased remarkable skills, rivaling or even exceeding human intelligence in certain areas. Their proficiency in translation is notable, as they may replicate the nuanced, preparatory steps of human translators for high-quality outcomes. Although there have been some notable work exploring using LLMs for code to code translation, there has not been one for smart contracts, especially when a target language is unseen to the LLM. In this work, we aim to introduce our novel framework SolMover consisting of two different LLMs working in tandem in a framework to understand coding concepts and then use that to translate code to an unseen language. We explore the human-like learning capability of LLMs in this paper with a detailed evaluation of the methodology to translate existing smart contracts from Solidity to a low-resource one called Move. Specifically, we enable one LLM to understand coding rules for the new language to generate a planning task, for the second LLM to follow, which does not have planning capability but does have coding. Experiments show that SolMOver brings significant improvement over gpt-3.5-turbo-1106 and outperforms both Palm2 and Mixtral-8x7B-Instruct. Our further analysis shows us that employing our bug mitigation technique even without the framework still improves code quality for all models.

Recent advances in blockchain research have been made in two important directions. One is refined resilience analysis utilizing game theory to study the consequences of selfish behavior of users (miners), and the other is the extension from a linear (chain) structure to a non-linear (graphical) structure for performance improvements, such as IOTA and Graphcoin. The first question that comes to mind is what improvements a blockchain system would see by leveraging these new advances. In this article, we consider three major properties for a blockchain system: α-partial verification, scalability, and finality-duration. We establish a formal framework and prove that no blockchain system can achieve α-partial verification for any fixed constant α, high scalability, and low finality-duration simultaneously. We observe that classical blockchain systems like Bitcoin achieve full verification (α =1) and low finality-duration, Ethereum 2.0 Sharding achieves low finality-duration and high scalability. We are interested in whether it is possible to partially satisfy the three properties.

With the recent advances in concepts like decentralized “cloud” and blockchain-enabled decentralized computing environments, the legacy modeling and orchestration tools developed to support centrally managed cloud-based ICT infrastructures are challenged by such a new paradigm built on top of decentralization. On the other hand, decentralized “cloud” and computing infrastructures need to support many Dapp use cases. As the complexity of these targeted application scenarios increases, there is an urgent need for developing automation and modeling tools for deploying and managing decentralized infrastructures. Instead of creating such tools from scratch, a natural approach is extending mature infrastructure modeling tools for Dapps and decentralized computing environments. To this end, in this work, we have developed extensions to the TOSCA domain-specific language to support smart contract specification of decentralized computing infrastructures for supporting Dapps, where smart contracts or chain codes manage a decentralized computing environment. The result is blockchain-based orchestration and automation for decentralized “cloud” and computing environments that use existing infrastructure as code tools to deploy and manage decentralized applications.

Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.

Trusted execution environment (TEE) technology has many uses, such as protecting data in the cloud and improving security for industrial IoT. However, there are technical challenges that limit its widespread adoption. These challenges include the fact that different TEE vendors have incompatible solutions, and devices equipped with the same TEE technology may belong to different owners, making it difficult to establish trust between them. To address these challenges and fully utilize TEE technology, a decentralized coordination mechanism called DHTee is proposed. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially attestation service. Devices equipped with TEE can interact securely with the blockchain to determine whether potential collaborating devices meet the requirements. DHTee is also flexible and can support new TEE schemes without affecting existing TEEs.

Function-as-a-service (FaaS) is an emerging computation architecture, which provides high scalability and flexibility. All the existing F aaS systems are owned and managed by a single cloud service provider. While this is not an issue for most existing enterprise applications, such character is not compatible with the decentralization principle of dApp/Web3 applications, more of which are being deployed in the cloud environment. Therefore, there is an urgent need to build a decentralized FaaS, which is managed by multiple cloud service providers and allows a decentralized application to take advantages of FaaS. In this research paper, we propose DeFaaS, a novel system for managing decentralized FaaS using blockchain technology and decentralized API management, where functions are executed on a distributed network of nodes by multi-cloud data centers, rather than on a centralized server. This allows for greater scalability and flexibility, as well as improved security and reliability.

With the recent advance in concepts like decentralized "cloud" and blockchain-enabled decentralized computing environments, the legacy modeling and orchestration tools developed to support centrally managed cloud-based ICT infrastructures are challenged by such a new paradigm built on top of decentralization. On the other hand, decentralized "cloud" and computing infrastructures need to support many Dapp use cases. As the complexity of these targeted application scenarios increases, there is an urgent need for developing automation and modeling tools for deploying and managing decentralized infrastructures. Instead of creating such tools from scratch, a natural approach is extending mature infrastructure modeling tools for Dapps and decentralized computing environments. To this end, in this work, we have developed extensions to the TOSCA domain-specific language to support smart contract specification of decentralized computing infrastructures for supporting Dapps, where smart contracts or chain codes manage a decentralized computing environment. The result is blockchain-based orchestration and automation for decentralized "cloud" and computing environments, which is a step forward for achieving full decentralization in general-purpose computing.

The resilience of a voting system has been a central topic in computational social choice. Many voting rules, like plurality, are shown to be vulnerable as the attacker can target specific voters to manipulate the result. What if a local differential privacy (LDP) mechanism is adopted such that the true preference of a voter is never revealed in pre-election polls? In this case, the attacker can only infer stochastic information about a voter's true preference, and this may cause the manipulation of the electoral result significantly harder. The goal of this paper is to provide a quantitative study on the effect of adopting LDP mechanisms on a voting system. We introduce the metric PoLDP (power of LDP) that quantitatively measures the difference between the attacker's manipulation cost under LDP mechanisms and that without LDP mechanisms. The larger PoLDP is, the more robustness LDP mechanisms can add to a voting system. We give a full characterization of PoLDP for the voting system with plurality rule and provide general guidance towards the application of LDP mechanisms.

In this paper, we present the design and implementation of a privacy preserving event based UTXO (Unspent Transaction Output) transaction system. Unlike the existing approaches that often depend on smart contracts where digital assets are first locked in a vault, and then released according to event triggers, the event based transaction system encodes event outcome as part of the UTXO note and safeguards event privacy by shielding it with zero-knowledge proof based protocols such that associations between UTXO notes and events are hidden from the validators. Without relying on any triggering mechanism, the proposed transaction system separates event processing from the transaction processing where confidential event based UTXO notes (event based UTXOs or conditional UTXOs) can be transferred freely with full privacy in an asynchronous manner, only with their asset values conditional to the linked event outcomes. The main advantage of such design is that it enables free trade of event based digital assets and prevents the assets from being locked. We implemented the proposed transaction system by extending the Zerocoin data model and protocols. The system is implemented and evaluated using xJsnark.

Iterative augmentation has recently emerged as an overarching method for solving Integer Programs (IP) in variable dimension, in stark contrast with the volume and flatness techniques of IP in fixed dimension. Here we consider 4-block n-fold integer programs, which are the most general class considered so far. A 4-block n-fold IP has a constraint matrix which consists of n copies of small matrices A, B, and D, and one copy of C, in a specific block structure. Iterative augmentation methods rely on the so-called Graver basis of the constraint matrix, which constitutes a set of fundamental augmenting steps. All existing algorithms rely on bounding the 𝓁₁- or 𝓁_∞-norm of elements of the Graver basis. Hemmecke et al. [Math. Prog. 2014] showed that 4-block n-fold IP has Graver elements of 𝓁_∞-norm at most 𝒪_FPT(n^{2^{s_D}}), leading to an algorithm with a similar runtime; here, s_D is the number of rows of matrix D and 𝒪_FPT hides a multiplicative factor that is only dependent on the small matrices A,B,C,D, However, it remained open whether their bounds are tight, in particular, whether they could be improved to 𝒪_FPT(1), perhaps at least in some restricted cases. We prove that the 𝓁_∞-norm of the Graver elements of 4-block n-fold IP is upper bounded by 𝒪_FPT(n^{s_D}), improving significantly over the previous bound 𝒪_FPT(n^{2^{s_D}}). We also provide a matching lower bound of Ω(n^{s_D}) which even holds for arbitrary non-zero lattice elements, ruling out augmenting algorithm relying on even more restricted notions of augmentation than the Graver basis. We then consider a special case of 4-block n-fold in which C is a zero matrix, called 3-block n-fold IP. We show that while the 𝓁_∞-norm of its Graver elements is Ω(n^{s_D}), there exists a different decomposition into lattice elements whose 𝓁_∞-norm is bounded by 𝒪_FPT(1), which allows us to provide improved upper bounds on the 𝓁_∞-norm of Graver elements for 3-block n-fold IP. The key difference between the respective decompositions is that a Graver basis guarantees a sign-compatible decomposition; this property is critical in applications because it guarantees each step of the decomposition to be feasible. Consequently, our improved upper bounds let us establish faster algorithms for 3-block n-fold IP and 4-block IP, and our lower bounds strongly hint at parameterized hardness of 4-block and even 3-block n-fold IP. Furthermore, we show that 3-block n-fold IP is without loss of generality in the sense that 4-block n-fold IP can be solved in FPT oracle time by taking an algorithm for 3-block n-fold IP as an oracle.

Industrial IoT (IIoT) is critical for industrial infrastructure modernization and digitalization. Therefore, it is of utmost importance to provide adequate protection of the IIoT system. A modern IIoT system usually consists of a large number of devices that are deployed in multiple locations and owned/managed by different entities who do not fully trust each other. These features make it harder to manage the system in a coherent manner and utilize existing security mechanisms to offer adequate protection. The emerging blockchain technology provides a powerful tool for IIoT system management and protection because the IIoT nature of distributed deployment and involvement of multiple stakeholders fits the design philosophy of blockchain well. Most existing blockchain construction mechanisms are not scalable enough and too heavy for an IIoT system. One promising way to overcome these limitations is utilizing hardware based trusted execution environment (TEE) in blockchain construction. However, most of the existing works on this direction do not consider the characteristics of IIoT devices (e.g., fixed functionality and limited supply) and face several limitations when they are applied for IIoT system management and protection, such as high energy consumption, single root-of-trust, and low decentralization level. To mitigate these challenges, we propose a novel field programmable gate array (FPGA) based blockchain system. It leverages the FPGA to build a simple but efficient TEE for IIoT devices, and removes the single root-of-trust by allowing all stakeholders to participate in the management of the devices. The FPGA based blockchain system shifts the computation/storage intensive part of blockchain management to more powerful computers but still involves the IIoT devices in the block construction to achieve a high level of decentralization. We implement the major FPGA components of the design and evaluate the performance of the whole system with a simulation tool to demonstrate its feasibility for IIoT applications.

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.

Audio fingerprinting techniques were developed to index and retrieve audio samples by comparing a content-based compact signature of the audio instead of the entire audio sample, thereby reducing memory and computational expense. Different techniques have been applied to create audio fingerprints; however, with the introduction of deep learning, new data-driven unsupervised approaches are available. This article presents Sequence-to-Sequence Autoencoder Model for Audio Fingerprinting (SAMAF), which improved hash generation through a novel loss function composed of terms: Mean Square Error, minimizing the reconstruction error; Hash Loss, minimizing the distance between similar hashes and encouraging clustering; and Bitwise Entropy Loss, minimizing the variation inside the clusters. The performance of the model was assessed with a subset of VoxCeleb1 dataset, a“speech in-the-wild” dataset. Furthermore, the model was compared against three baselines: Dejavu, a Shazam-like algorithm; Robust Audio Fingerprinting System (RAFS), a Bit Error Rate (BER) methodology robust to time-frequency distortions and coding/decoding transformations; and Panako, a constellation-based algorithm adding time-frequency distortion resilience. Extensive empirical evidence showed that our approach outperformed all the baselines in the audio identification task and other classification tasks related to the attributes of the audio signal with an economical hash size of either 128 or 256 bits for one second of audio.

This paper proposes SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection. SafeDB provides a comprehensive and systematic hardware-based security framework from the bitstream protection to data confidentiality, especially for the cloud environment. The AES key shared between FPGA and client for the bitstream encryption is generated in hard-wired logic using PKI and ECC. The data security is assured by the enclaved processing with encrypted data, meaning that the encrypted data is processed inside the FPGA fabric. Thus, no one in the system is able to look into clients' data because plaintext data are not exposed to memory and/or memory-mapped space. SafeDB is resistant not only to the side channel attack but to the attacks from malicious insiders. We have constructed an 8-node cluster prototype with Zynq UltraScale+ FPGAs to demonstrate the security, performance, and practicability.

Bribery in election (or computational social choice in general) is an important problem that has received a considerable amount of attention. In the classic bribery problem, the briber (or attacker) bribes some voters in attempting to make the briber’s designated candidate win an election. In this paper, we introduce a novel variant of the bribery problem, “Election with Bribed Voter Uncertainty” or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted. This uncertainty occurs either because a bribed voter may not cast its vote in fear of being caught, or because a bribed voter is indeed caught and therefore its vote is discarded. As a first step towards ultimately understanding and addressing this important problem, we show that it does not admit any multiplicative O(1)-approximation algorithm modulo standard complexity assumptions. We further show that there is an approximation algorithm that returns a solution with an additive-ε error in FPT time for any fixed ε.

Researchers deal with the class imbalanced problem in many real-world applications and GAN based data augmentation is considered as an efficient approach to address this problem. GANs need a huge training data to generate efficient augmented data. However, the required sufficient training data is not available in many research areas. In this paper, we introduce a new concept called virtual big data to address this problem. We prove that, virtual big data can provide the GANs sufficient training data to generate efficient augmented data with less mode collapse and vanishing generator gradients problems. We show that, the curse of dimensionality which is considered as a negative factor in machine learning can play a positive role to solve vanishing generator gradients via making discriminator less perfect. First, we transform the training data from n dimensional space into m dimensional space where, m = c * n and c is concatenation factor. To do so, c different training instances are selected and concatenated to each other to form a c * n dimensional instance. Increasing the dimension of training data from n to c * n is key to increase the number of training instances from N to C(N, c). Transformed training data are called virtual big data since they differ original training instances in terms of size and dimension. Our experiments show that, V-GAN, a GAN trained by virtual big data can outperform standard GANs when it comes to deal with extremely scarce training data. Furthermore, V-GAN can outperform traditional oversampling techniques in terms of precision, F1 score and Area Under Curve (AUC) score.

We consider the electoral bribery problem in computational social choice. In this context, extensive studies have been carried out to analyze the computational vulnerability of various voting (or election) rules. However, essentially all prior studies assume a deterministic model where each voter has an associated threshold value, which is used as follows. A voter will take a bribe and vote according to the attacker's (i.e., briber's) preference when the amount of the bribe is above the threshold, and a voter will not take a bribe when the amount of the bribe is not above the threshold (in this case, the voter will vote according to its own preference, rather than the attacker's). In this paper, we initiate the study of a more realistic model where each voter is associated with a willingness function, rather than a fixed threshold value. The willingness function characterizes the likelihood a bribed voter would vote according to the attacker's preference; we call this bribe-effect uncertainty. We characterize the computational complexity of the electoral bribery problem in this new model. In particular, we discover a dichotomy result: a certain mathematical property of the willingness function dictates whether or not the computational hardness can serve as a deterrence to bribery attackers.

In cloud computing, it is often assumed that cloud vendors are trusted; the guest Operating System (OS) and the Virtual Machine Monitor (VMM, also called Hypervisor) are secure. However, these assumptions are not always true in practice and existing approaches cannot protect the data privacy of applications when none of these parties are trusted. We investigate how to cope with a strong threat model which is that the cloud vendors, the guest OS, or the VMM, or both of them are malicious or untrusted, and can launch attacks against privacy of trusted user applications. This model is relevant because applications may be small enough to be formally verified, while the guest OS and VMM are too complex to be formally verified. Specifically, we present the design and analysis of an architectural solution which integrates a set of components on-chip to protect the memory of trusted applications from potential software and hardware based attacks from untrusted cloud providers, compromised guest OS, or malicious VMM. Full-system performance evaluation results show that the design only incurs 9 percent overhead on average, which is a small performance price that is paid for the substantial security gain.

Increasing complexity of integrated circuits and IP-based hardware designs have created the risk of hardware Trojans. This paper introduces a new type of threat, a coherence-exploiting hardware Trojan. This Trojan can be maliciously implanted in master components in a system, and continuously injects memory transactions onto the main interconnect. The injected traffic forces the eviction of cache lines, taking advantage of cache coherence protocols. This type of Trojans insidiously slows down the system performance, incurring Denial-of-Service (DoS) attack. We used a Xilinx Zynq-7000 device to implement the Trojan and evaluate its severity. Experiments revealed that the system performance can be severely degraded as much as 258% with the Trojan. A countermeasure to annihilate the Trojan attack is proposed in detail. We also found that AXI version 3.0 supports a seemingly irrelevant invalidation protocol through ACP, opening a door for the potential Trojan attack.

Modern supply chain is a complex system and plays an important role for different sectors under the globalization economic integration background. Supply chain management system is proposed to handle the increasing complexity and improve the efficiency of flows of goods. It is also useful to prevent potential frauds and guarantee trade compliance. Currently, most companies maintain their own IT system for supply chain management. However, this approach has some limitations that prevent one to get most of the supply chain information. Using emerging decentralized ledger technology to build supply chain management system is a promising direction. However, decentralized ledger usually suffers from low performance and lack of capability to protect information stored on the ledger. To overcome these challenges, we propose CoC, a novel supply chain management system based on hybrid decentralized ledger. We develop an efficient block construction method with the model and security mechanism to prevent unauthorized access to data stored on the ledger.

Blockchain, or distributed ledger, provides a way to build various decentralized systems without relying on any single trusted party. This is especially attractive for smart contracts, that different parties do not need to trust each other to have a contract, and the distributed ledger can guarantee correct execution of the contract. Most existing distributed ledger based smart contract systems process smart contracts in a serial manner, i.e., all users have to run a contract before its result can be accepted by the system. Although this approach is easy to implement and manage, it is not scalable and greatly limits the system's capability of handling a large number of smart contracts. In order to address this problem, we propose a scalable smart contract execution scheme that can run multiple smart contract in parallel to improve throughput of the system. Our scheme relies on two key techniques: a fair contract partition algorithm leveraging integer linear programming to partition a set of smart contracts into multiple subsets, and a random assignment protocol assigning subsets randomly to a subgroup of users. We prove that, our scheme is secure as long as more than 50% of the computational power is possessed by honest nodes. We then conduct experiments with data from existing smart contract system to evaluate the efficiency of our scheme. The results demonstrate that our approach is scalable and much more efficient than the existing smart contract platform.

Transaction system build on top of blockchain, especially smart contract, is becoming an important part of world economy. However, there is a lack of formal study on the behavior of users in these systems, which leaves the correctness and security of such system without a solid foundation. Unlike mining, in which the reward for mining a block is fixed, different execution results of a smart contract may lead to significantly different payoffs of users, which gives more incentives for some user to follow a branch that contains a wrong result, even if the branch is shorter. It is thus important to understand the exact probability that a branch is being selected by the system. We formulate this problem as the (+-)-Biased Ballot Problem as follows: there are n voters one by one voting for either of the two candidates A and B. The probability of a user voting for A or B depends on whether the difference between the current votes of A and B is positive or negative. Our model takes into account the behavior of three different kinds of users when a branch occurs in the system -- users having preference over a certain branch based on the history of their transactions, and users being indifferent and simply follow the longest chain. We study two important probabilities that are closely related with a blockchain based system - the probability that A wins at last, and the probability that A receives d votes first. We show how to recursively calculate the two probabilities for any fixed n and d, and also discuss their asymptotic values when n and d are sufficiently large.

Elliptic curve based cryptography has attracted a lot of attention because these schemes usually require less storage than those based on finite field. It is also used to construct bilinear pairing, which is an essential tool to construct various cryptography schemes. The security of a large portion of these schemes depends on the hardness of ECDLP. Unlike discrete logarithm problem on finite field and integer factorization problem, currently there is no sub-exponential algorithm for general ECDLP, and parallel collision search is the most effective approach. Using parallel collision search for ECDLP is not only computation intensive but also storage intensive. Therefore, it requires a large number of machines to collaborate to finish the job. Considering all these requirements, we propose a solution for ECDLP using MapReduce and parallel collision search in the cloud environment, which can be scaled to involve a huge number of computation nodes. We implement the solution using Amazon EC2, and the experiment results show its scalability and effectiveness.

Big data comprises high-volume, high-velocity, and high-variety information assets that demand cost effective and innovative forms of information processing for enhanced insight and decision making. The rise of cloud computing makes providing flexible computation, communication, and storage capacity possible. Due to the outsourcing and sharing feature of cloud computing, security becomes one of the main concerns. Both the data and program are potential targets for security compromise. These concerns hinder the end users to migrate to the cloud for big data processing. A lot of techniques have been developed to alleviate the security concerns for big data processing in the cloud environment. However, these approaches usually only focus on data protection or rely on certain security anchor in the cloud environment. We propose a formal framework and security definition of big data processing which takes both data and program protection into consideration. The framework/security definition captures the key features of the scenario and avoids sinking into unnecessary details. We develop a solution under this framework which combines operation steganography and FHE scheme to satisfy the security definition.

Database outsourcing is an important application of cloud computing, and security is one of the most critical concerns in adopting this application model, such as data privacy, query privacy, etc. Data integrity is another essential requirement for outsourced database system. When the database is outsourced to public cloud, the situation is more complex as different users may modify the data and these users may hold different privileges for different parts of the database. Furthermore, as the cloud is in charge of the management of the database, users have to rely on the cloud to guarantee data integrity. We propose ABSS to protect the integrity of outsourced database which supports fine-grained modification policy. ABSS utilizes an attribute based sanitizable signature scheme, which combining the ingredients of attribute based encryption and sanitizable signature. ABSS enables the database owner to deploy fine-grained policy of database modification and can detect illegal modifications without trusting the cloud. We also discuss the security properties and performance of ABSS to show its practicability.

We present a micro-architecture based lightweight framework to enhance dependability and security of software against code reuse attack. Different from the prior hardware based approaches for mitigating code reuse attacks, our solution is based on software diversity and instruction level control flow randomization. Generally, software based instruction location randomization (ILR) using binary emulator as a mediation layer has been shown to be effective for thwarting code reuse attacks like return oriented programming (ROP). However, our in-depth studies show that straightforward and naive implementation of ILR at the micro-architecture level will incur major performance deficiencies in terms of instruction fetch and cache utilization. For example, straightforward implementation of ILR increases the first level instruction cache miss rates on average by more than 9 times for a set of SPEC CPU2006 benchmarks. To address these issues, we present a novel micro-architecture design that can support native execution of control flow randomized software binary while at the same time preserve the performance of instruction fetch and efficient use of on-chip caches. The proposed design is evaluated by extending cycle based x86 architecture simulator, XIOSim with validated power simulation. Performance evaluation on SPEC CPU2006 benchmarks shows an average speedup of 1.63 times compared to the hardware implementation of ILR. Using the proposed approach, direct execution of ILR software incurs only 2.1% IPC performance slowdown with a very small hardware overhead.

Privacy is one of the critical concerns that hinder the adoption of public cloud. For storage, encryption can be used to protect user's data. But for outsourced data processing, for example MapReduce, there is no satisfying solution. Users have to trust the cloud service providers totally. In this work, we propose PFC, a FPGA cloud for privacy preserving computation in the public cloud environment. PFC leverages the security feature of the existing FPGAs originally designed for bitstream IP protection and proxy re-encryption for preserving user data privacy. In PFC, cloud service providers are not necessarily trusted, and during outsourced computation, user's data is protected by a data encryption key only accessible by trusted FPGA devices. As an important application of cloud computing, we apply PFC to the popular MapReduce programming model and extend the FPGA based MapReduce pipeline with privacy protection capabilities. Proxy re-encryption is employed to support dynamic allocations of trusted FPGA devices as mappers and reducers. Finally, we conduct evaluation to demonstrate the effectiveness of PFC.

Device scaling engineering is facing major challenges in producing reliable transistors for future electronic technologies. With shrinking device sizes, the total circuit sensitivity to both permanent and transient faults has increased significantly. Research for fault tolerant processors has primarily focused on the conventional processor architectures. Neural network computing has been employed to solve a wide range of problems. This paper presents a design and implementation of a physical neural network that is resilient to permanent hardware faults. To achieve scalability, it uses tiled neuron clusters where neuron outputs are efficiently forwarded to the target neurons using source based spanning tree routing. To achieve fault resilience in the face of increasing number of permanent hardware failures, the design pro-actively preserves neural network computing performance by selectively replicating performance critical neurons. Furthermore, the paper presents a spanning tree recovery solution that mitigates disruption to distribution of neuron outputs caused by failed neuron clusters. The proposed neuron cluster design is implemented in Verilog. We studied the fault resilience performance of the described design using a RBM neural network trained for classifying handwritten digit images. Results demonstrate that our approach can achieve improved fault resilience performance by replicating only 5% most important neurons.

In today's technology driven world, it is essential to build secure systems with low faulty behavior. Authentication is one of the primary means to gain access to secure systems. Users need to be authenticated in order to gain access to the services and sensitive information contained within the system. Due to the surge in the number of touch based smart devices, there arises a need for a compatible authentication system. Historically, fingerprints have served in its fullest capacity to establish the uniqueness of an individual's identity. It can be detected using capacitive sensing techniques. In this paper we present a novel unified device using transparent electronics for both fingerprint scan and multi-touch interaction. We discuss a high resolution transparent touch sensitive device and a read out circuit that drives the capacitive sensor array for touch interactions at low resolutions and for fingerprint sensing at higher resolutions. Using circuit simulation and custom Verilog-A model for transparent thin-film transistors, we verified that our design can sense fingerprints in 8.25 ms and detect touches in 0.6ms with an efficient power consumption of 1 mW. The results show that such a device can be realized and can serve as a very efficient means of user authentication. Furthermore, from the usability aspect, the proposed device is essential as it provides user transparent and non intrusive authentication.

We present a lightweight hardware framework for providing high assurance detection and prevention of code injection attacks using a lockstep diversified shadow execution. Recent studies show that hardware diversification can detect software attacks by checking the consistency of their behavior simultaneously. Unfortunately, the severe performance degradation and extra system costs caused by these methods are unacceptable in many applications. This paper presents a hardware-level, lockstep shadow thread framework to enrich the diversity of the software execution, with the facilitation from programmable hardware decoder and novel CPU support of tightly coupled shadow thread technique. Specifically, given a piece of (legacy) binary code, we first generate diversified binary versions using an offline binary rewriter and programmable hardware binary translator at runtime. Two diversified binary code images are launched as dual simultaneous threads in the hardware layer with one as the primary thread and the other one as shadow thread. Instructions from the shadow thread are not executed but just compared, and thus incur no OS side-effects. The extended CPU is able to decode instructions from both threads, and dispatch them to the next stage pipeline for a lockstep comparison. Any mismatch of the decoded instructions from the two threads caused by remotely injected binary code will be detected. Our design provides instruction set randomization (ISR) with minimal cost in performance, when compared with straightforward ISR implementation. The simulation results indicate that our framework incurs very small overheads and provides a protection against code injection attacks.

Read-mapping, i.e., finding certain patterns in a long DNA sequence, is an important operation for molecular biology. It is widely used in a variety of biological analyses including SNP discovery, genotyping and personal genomics. As next-generation DNA sequencing machines are generating an enormous amount of sequence data, it is a good choice to implement the read-mapping algorithm in the MapReduce framework and outsource the computation to the cloud. Data privacy becomes a big concern in this situation as DNA sequences are very sensitive. In response, encryption may be used to protect the data. However, it is very difficult for the cloud to process cipher texts. In the MapReduce framework, even if values (data to be processed) may be protected by encryption, keys cannot be encrypted using sematic secure encryption schemes as it will affect the MapReduce scheduling mechanism. But if no protection is utilized, attackers may extract useful information from unprotected keys. We propose a solution that can securely outsource read-mapping computations in the MapReduce framework by leveraging inherent tamper resistant properties of FPGAs. We also provide a method to protect the keys generated in this process. We implement our solution using FPGAs and apply it to some data sets. The security evaluation and experimental results show that with this method, DNA sequence privacy is well protected, and the extra cost is acceptable.

Behavioral biometrics have recently begun to gain attention for mobile user authentication. The feasibility of touch gestures as a novel modality for behavioral biometrics has been investigated. In this paper, we propose applying a statistical touch dynamics image (aka statistical feature model) trained from graphic touch gesture features to retain discriminative power for user authentication while significantly reducing computational time during online authentication. Systematic evaluation and comparisons with state-of-the-art methods have been performed on touch gesture data sets. Implemented as an Android App, the usability and effectiveness of the proposed method have also been evaluated.

Increasingly, cyber attacks (e.g., kernel rootkits) target the inner rings of a computer system, and they have seriously undermined the integrity of the entire computer systems. To eliminate these threats, it is imperative to develop innovative solutions running below the attack surface. This paper presents MGuard, a new most inner ring solution for inspecting the system integrity that is directly integrated with the DRAM DIMM devices. More specifically, we design a programmable guard that is integrated with the advanced memory buffer of FB-DIMM to continuously monitor all the memory traffic and detect the system integrity violations. Unlike the existing approaches that are either snapshot-based or lack compatibility and flexibility, MGuard continuously monitors the integrity of all the outer rings including both OS kernel and hypervisor of interest, with a greater extendibility enabled by a programmable interface. It offers a hardware drop-in solution transparent to the host CPU and memory controller. Moreover, MGuard is isolated from the host software and hardware, leading to strong security for remote attackers. Our simulation-based experimental results show that MGuard introduces no speed overhead, and is able to detect nearly all the OS-kernel and hypervisor control data related rootkits we tested.

As cloud diversifies into different application fields, understanding and characterizing the specific work load sand application requirements play important roles in the design of efficient cloud infrastructure and system software support. Video analytic is a rapidly advancing field and it is widely used in many application domains (i.e., health, medical care, surveillance, and defense). To support video analytic applications efficiently in cloud, one has to overcome many challenges such as lack of understanding of the relationship and trade off between analytic performance metrics and resource requirements. Furthermore, cloud computing has grown from the early model of resource sharing to data sharing and workflow sharing. To address the challenges and to lever age emerging trends, we propose and experiment with a domain specific cloud environment for video analytic applications. We design a cloud infrastructure framework for sharing video data, analytic software, and workflow. In addition, we create a video analytic quality aware resource plan model to guarantee users QoS and optimize usage of resources based on predictive knowledge of video analytic softwares performance metrics and a resource planning model that optimizes the overall analytic service quality under users constraints (i.e., time and cost).The predictive knowledge is represented as input and analytic software specific predictors. The experimental results show that the video analytic quality aware resource planning model can balance the tradeoff between analytic quality and resource requirements, and achieve optimal or near-optimal planning for video analytic workloads with constraints in a resource shared environment. Simulation studies show that resource planning results using ground truth and video analytic performance predictions are very similar, which indicates that our analytic quality/resource predictors are very accurate.

In this paper, we present a novel approach of using the integrated GPU to accelerate conventional operations that are normally performed by the CPUs, the bulk memory operations, such as memcpy or memset. Offloading the bulk memory operations to the GPU has many advantages, i) the throughput driven GPU outperforms the CPU on the bulk memory operations; ii) for on-die GPU with unified cache between the GPU and the CPU, the GPU private caches can be leveraged by the CPU for storing moved data and reducing the CPU cache bottleneck; iii) with additional lightweight hardware, asynchronous offload can be supported as well; and iv) different from the prior arts using dedicated hardware copy engines (e.g., DMA), our approach leverages the exiting GPU hardware resources as much as possible. The performance results based on our solution showed that offloaded bulk memory operations outperform CPU up to 4.3 times in micro benchmarks while still using less resources. Using eight real world applications and a cycle based full system simulation environment, the results showed 30% speedup for five, more than 20% speedup for two of the eight applications.

Electrophoretic displays (EPDs) and organic light emitting diode (OLEDs) are two key technologies used in mobile de-vices. In this paper, we propose the design of an integrated hybrid display combining a transparent OLED (TOLED) and a low power EPD, which is adaptive to show contents of a frame partially on either the TOLED or the EPD. A windows-based predictive model and a calibration algorithm on TOLED are introduced to decide how frame contents can be split between the two displays for achieving the best tradeoff between power reduction and user experiences. A simulation environment that can estimate both the energy consumption and optical properties of the proposed hybrid display is set up based on actual physical measurements. Simulation results show that the predictive model can make right decisions on choosing proper displays in over 90% of the test cases, and this new display design can save over 70% power under many mobile application contexts and still sup-port contents that require fast update rates.

Recent advances in virtualization technology and wide acceptance of the cloud computing model are having significant impact on the software service industry. Though cloud computing and virtualization technology has been widely applied in supporting the information processing needs of conventional enterprise and business applications, there has been little success to-date in enabling realtime 3D virtual appliances in the cloud. This paper aims to address this deficiency by presenting SHARC, a solution for enabling scalable support of realtime 3D applications in a cloud computing environment. The solution uses a scalable pipelined processing infrastructure which consists of three processing networks according to the principle of division-of-labor, a virtualization server network for running 3D virtual appliances, a graphics rendering network for processing graphics rendering workload with load balancing, and a media streaming network for transcoding rendered frames into H.264/MPEG-4 media streams and streaming the media streams to a cloud user. The paper describes a prototype implementation of SHARC and reports test results that demonstrate the viability of this approach.

Existing mechanisms for querying wireless sensor networks leak client interests to the servers performing the queries. The leaks are not only in terms of specific regions of interest but also of client access patterns. In this article we introduce the problem of preserving the privacy of clients querying a wireless sensor network owned by untrusted organizations. We first propose an efficient protocol, SPYC, that ensures full client privacy in settings where the servers providing access to the network are honest-but-curious and whose collaboration does not extend beyond well-defined administrative purposes. Furthermore, we study the same query privacy problem in a setting where servers exhibit malicious behavior or where powerful external attackers have access to sensor network traffic information. In this setting we propose two metrics for quantifying the privacy achieved by a client's query sequence. We then extend SPYC with a suite of practical algorithms, then analyze the privacy and efficiency levels they provide. Our TOSSIM simulations show that the proposed extensions are communication efficient while significantly improving client privacy levels.

Natural ways of input greatly enhance the entertainment experience for emerging gaming systems represented by the Sony Playstation 2 EyeToy and the Nintendo Wii Console. In this paper we present a novel method of using human body gestures depth image as gaming application input. Depth images have natural advantages over grayscale or color images in terms of robustness against illumination change, texture complexity, and background interference. Our proposed method consists of three major components: depth image acquisition, mean shift based preprocessing, and HMM-based gesture recognition. We validate our method by applying it to a boxing game scenario to distinguish boxing gestures such as dodge, jab, hook, and uppercut. The experiment results indicate that our method can efficiently distinguish the subtle differences among these gestures and yield excellent accuracy (up to about 98%). The potential usage of the proposed method on gaming applications and generic human computer interaction is very promising.

In a large-scale mobile gaming environment with limited wireless network bandwidth, efficient mechanisms for state update are crucial to allow graceful real-time interaction for a large number of players. By using the state updating threshold as a key parameter that bridges the resulting state distortion and the network traffic, we are able to study the fundamental traffic-distortion tradeoffs via both theoretical modeling and numerical analysis using real game traces. We consider a WiMAX link model, where the bandwidth allocation is driven by the underlying physical layer link quality as well as application layer gaming behaviors. Such a cross-layer optimization problem can be solved using standard convex programming techniques. By exploring the temporal locality of gaming behavior, we also propose a prediction method for on-line bandwidth adaptation. Using real data traces from a multiplayer driving game, TORCS, the proposed network-aware bandwidth allocation method (NABA) is able to achieve significant reduction in state distortion compared to two baselines: uniform and proportional policies.

In large scale mobile gaming environments, efficient mechanisms for state update is crucial to allow graceful real-time interaction of a large number of players, under limited wireless network bandwidth. By using the state updating threshold as a key parameter that bridges the resulting state inconsistency (or distortion) and the network traffic, we are able to model the fundamental traffic-distortion tradeoffs. Given the tradeoffs for all users, an optimal bandwidth allocation can be derived using well-known convex programming techniques. By exploring the temporal locality of gaming behavior, we also propose a prediction method to realize on-line bandwidth adaptation. Using real data trace from a multi-player driving game, the proposed network aware bandwidth allocation method, NABA, is able to achieve significant reduction in state distortion compared to two baselines: Uniform and Proportional policies. NABA is also adaptable to variations in system parameters, including bandwidth constraint and network delay.