Sparse General Matrix–Matrix Multiplication (SpGEMM) is a fundamental but computationally intensive operation that underpins many scientific workloads, including numerous AI applications. With the increasing demands for data security, privacy-preserving computation techniques, such as Fully Homomorphic Encryption (FHE), have gained significant attention for their ability to process sensitive data without decryption. Nonetheless, executing SpGEMM within the framework of FHE presents significant challenges. The most effective SpGEMM algorithms exploit matrix sparsity to minimize computational costs; however, FHE obscures both the data values and the sparsity structures. Prior FHE‑based privacy‑preserving computation frameworks either ignore the inherent sparsity of matrices and rely on dense General Matrix–Matrix Multiplication (GEMM), incurring substantial overhead from redundant homomorphic multiplications, or they attempt to exploit sparsity by encrypting only the non‑zero values, which inadvertently exposes sensitive positional information. To address this gap and achieve a better balance between efficiency and privacy, we propose CipherSkip, an efficient FHE-compatible SpGEMM framework that enables oblivious data and position processing under a Single Instruction Multiple Data (SIMD) scheme. Moreover, we extend our method to support an arbitrary number of sparse matrices (FHE-SpGEMCM). The efficiency analysis shows that our method achieves an average homomorphic computation cost of (nAnB)2/n2N, where nA and nB represent the number of nonzero elements in A and B respectively, n is the shared inner dimension of the multiplication, and N denotes the batch size used in FHE. Experimental results demonstrate that for square matrices of scale 29, our scheme achieves an average speedup of 439.25 × and a 10.68 × reduction in memory consumption compared to state-of-the-art baselines that ignore sparsity. Furthermore, when the scale increases to 213, our method yields up to a 1201.77 × speedup over baselines that only exploit the sparsity of a single matrix.

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.

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.

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software IP protection techniques. To secure these IPs, researchers have explored the domain of Intellectual Property Protection (IPP) using different approaches. In this paper, we discuss the existing IP rights and concurrent breakthroughs in the field of IPP research; provide discussions on hardware IP and software IP attacks and defense techniques; summarize different applications of IP protection; and lastly, identify the challenges and future research prospects in hardware and software IP security.

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.

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is used for diverse tasks such as accessing online information, making phone calls, purchasing items, and controlling the smart home. Echo offers user-friendly voice interaction to automate everyday tasks making it a massive success. Though many people view Amazon Echo as a helpful assistant at home or office, few know its underlying security and privacy implications. In this paper, we present the findings of our research on Amazon Echo’s security and privacy concerns. The findings are divided into different categories by vulnerability or attacks. The proposed mitigation(s) to the vulnerabilities are also presented in the paper. We conclude that though numerous privacy concerns and security vulnerabilities associated with the device are mitigated, many vulnerabilities still need to be addressed.

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.

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.

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.

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.

We propose CL-PRE, a certificateless proxy re-encryption scheme for secure data sharing with public cloud, which leverages maximal cloud resources to reduce the computing and communication cost for data owner. Towards running proxy in public cloud environment, we further propose multi-proxy CL-PRE and randomized CL-PRE, which enhance the security and robustness of CL-PRE. We implement all CL-PRE schemes and evaluate their security and performance.