To address the growing privacy concerns in cloud computing, homomorphic encryption (HE) provides a secure computation framework that enables functions to be evaluated directly on encrypted data without revealing the underlying plaintext.However, the practical deployment of HE remains limited due to its high computational overhead and the lack of native support for HE-unfriendly operations, such as division, conditional branching, and logarithmic functions.To mitigate these limitations, recent studies have proposed computation paradigms based on precomputed lookup tables (LUTs), which allow HE-unfriendly functions to be evaluated indirectly by querying encrypted precomputed tables.This paper proposes a non-interactive model based on word-wise homomorphic encryption for evaluating arbitrary one-input functions over encrypted LUTs.Existing LUT-based approaches built on BFV or BGV schemes require precomputing outputs for all possible inputs, leading to substantial computational overhead, and are restricted to integer-valued computations.To overcome these limitations, we introduce a new LUT-based evaluation method by leveraging the Cheon-Kim-Kim-Song (CKKS) scheme, which supports approximate real-number arithmetic.Furthermore, our method is extended to support multiple real-number inputs, making it the first LUTbased HE approach capable of handling multi-input real-number functions.In addition, we design a structured rearrangement strategy during the LUT construction phase, which effectively reduces the overall computational cost during function evaluation.Experimental results demonstrate that the proposed method enables flexible and efficient real-number function evaluation while significantly reducing latency compared to existing approaches.In particular, for two-input function evaluation, our method achieves speedups ranging from approximately 1.14× to 3.40× over prior methods and for three-input function evaluation, achieves speedups ranging from approximately 1.09× to 1.16×.

OpenLVLM-MIA is a new benchmark that highlights fundamental challenges in evaluating membership inference attacks (MIA) against large vision-language models (LVLMs). While prior work has reported high attack success rates, our analysis suggests that these results often arise from detecting distributional bias introduced during dataset construction rather than from identifying true membership status. To address this issue, we introduce a controlled benchmark of 6,000 images where the distributions of member and non-member samples are carefully balanced, and ground-truth membership labels are provided across three distinct training stages. Experiments using OpenLVLM-MIA demonstrated that the performance of state-of-the-art MIA methods approached chance-level. OpenLVLM-MIA, designed to be transparent and unbiased benchmark, clarifies certain limitations of MIA research on LVLMs and provides a solid foundation for developing stronger privacy-preserving techniques.

To address the growing security challenges in cloud computing, homomorphic encryption (HE) offers a promising solution for a third party to evaluate functions and maintain data privacy even during calculation, as it allows for the evaluation on ciphertexts. However, the practical use of HE is constrained by limited arithmetic capabilities and high computational overhead. A promising strategy to mitigate these challenges is to evaluate HE-unfriendly functions through precomputed lookup tables (LUTs). This paper proposes a non-interactive model for evaluating arbitrary one-input functions using homomorphic encrypted LUTs with word-wise HE. Existing methods based on BFV/BGV schemes require precomputing outputs for all possible inputs, which leads to significant computational overhead. Furthermore, they are restricted to integer computations. This paper proposes a new LUT-based method that extends these approaches by employing the CKKS scheme, which supports approximate realnumber computation, thereby enhancing flexibility and efficiency. Our CKKS-based LUT computation method enables a flexible trade-off between accuracy and latency while supporting realnumber operations. Specifically, increasing the LUT size improves the output accuracy for a given function, allowing users to balance computational precision and processing speed according to their needs. Our experimental evaluation confirms that the proposed method achieves up to a $19 \%$ reduction in latency and up to an $\mathbf{8 1. 1 \%}$ reduction in MAPE compared to baselines with larger domains, i.e., plaintext space, while maintaining acceptable memory consumption.

Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

It is our great pleasure to welcome you to the 8th IEEE International Workshop on Big Data and IoT Security in Smart Computing (BITS 2024) co-located with the 10th IEEE International Conference on Smart Computing (SMARTCOMP 2024). This year, the BITS 2024 is held in person in Osaka, Japan.

The tenth IEEE International Conference on Smart Computing (SmartComp 2024), sponsored by the IEEE Computer Society, will be held in-person in Osaka, Japan. It continues the tradition of the previous editions in presenting high-quality research and technology in smart and connected computing.

To protect sensitive data in cloud outsourcing smart systems, we implemented a private function evaluation system with fully homomorphic encryption (FHE) and trusted execution environment (TEE) using lookup tables (LUT), which preserves the privacy of data in various smart systems, including anomaly detection in smart grids. The proposed system introduces a trusted authority (TA) to manage FHE keys and generate private information retrieval (PIR) queries to extract the output from the LUT. The remaining problem in our previous work is that the TA knows the index distribution of data points in LUTs and the index of matched data points, which may cause security issues. In this work, we solve the security issues by executing operations in the TA with the TEE and reducing the runtime using unencrypted LUTs. Our system allows the computation of arbitrary multi-input functions to improve efficiency and expand the scope of applications with FHE in smart systems. We confirmed the evaluation runtime of an 18-bit one-input function needs 2.49 s. Compared to our previous work, our system achieved 2.1x speed up for an 8-bit three-input function with one thread.

Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can substantially enhance the precision of search and recommendation systems. Existing methods frame NER as a sequence labeling task, which requires extra data and, therefore may be limited in terms of sustainability. One promising solution is to employ a Machine Reading Comprehension (MRC) approach for NER tasks, thereby eliminating the dependence on additional data. This process encounters key challenges, including: (1) Unconventional predictions; (2) Inefficient multi-stream processing; (3) Absence of a proficient reasoning strategy. To this end, we present the Single-Stream Reasoner (SSR), a solution utilizing a reasoning strategy and standardized inputs. This yields a type-agnostic solution for both flat and nested NER tasks, without the need for additional data. On ten NER benchmarks, SSR achieved state-of-the-art results, highlighting its robustness. Furthermore, we illustrated its efficiency through convergence, inference speed, and low-resource scenario performance comparisons. Our architecture displays adaptability and can effortlessly merge with various foundational models and reasoning strategies, fostering advancements in both the IR and IE fields.

Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. First, we trained a model using transformer-based pre-trained language models, BERT, on a text-based dataset for the automatic detection of dark patterns in e-commerce. Then, we applied post-hoc explanation techniques, including local interpretable model agnostic explanation (LIME) and Shapley additive explanations (SHAP), to the trained model, which revealed which terms influence each prediction as a dark pattern. In addition, we extracted and analyzed terms that affected the dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services. Our code is available at https://github.com/yamanalab/whydarkpattern.

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points ( endpoint location privacy ) and the route itself ( route privacy ). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼ 25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8).

This study addresses the privacy problems of data provided by multiple data owners for range query processing on the cloud. Although existing methods preserve data privacy against the cloud or data analysts who receive query responses, protecting data privacy from both remains a challenge. Combining differential privacy (DP) and homomorphic encryption (HE) to construct differentially private outputs over encrypted raw data is a promising way to avoid data privacy leakage to the cloud with encryption while protecting data privacy from data analysts with DP. Although DP adopts several partitioning algorithms to achieve small noise, partitioning cannot be executed once the data is encrypted. In this paper, we propose a new HE-friendly privacy-preserving partitioning algorithm satisfying DP. Although HE enables operations over encrypted data, the execution time of such primitive arithmetic operations is approximately 109times slower than without encryption. Therefore, it is mandatory to reduce the calculation complexity. The proposed partitioning method, which only compares every next-to-each-other data to merge, achieves $\mathcal{O}\left( n \right)$ calculation complexity, where n is the domain size of the input histograms, whereas the greedy algorithm requires $\mathcal{O}\left( {{2^n}} \right)$. The experimental evaluation showed that the execution time of the proposed algorithm for 4,096-domain-size data was approximately 4 h and 35 min, which was acceptable when creating a data summary for the range query processing system and not targeting on-the-fly adoption of DP. Additionally, we confirmed that the accuracy of the proposed algorithm was equivalent to that of the state-of-the-art partitioning algorithm.

Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.’s study in 2019 [1], which consists of 1,818 dark pattern texts from shopping sites. Then, we added negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.’s dataset. We also applied state-of-the-art machine learning methods to show the automatic detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975 with RoBERTa. The dataset and baseline source codes are available at https://github.com/yamanalab/ec-darkpattern.

Multiple-choice question answering (MCQA) for machine reading comprehension (MRC) is challenging. It requires a model to select a correct answer from several candidate options related to text passages or dialogue. To select the correct answer, such models must have the ability to understand natural languages, comprehend textual representations, and infer the relationship between candidate options, questions, and passages. Previous models calculated representations between passages and question-option pairs separately, thereby ignoring the effect of other relation-pairs. In this study, we propose a human reading comprehension attention (HRCA) model and a passage-question-option (PQO) matrix-guided HRCA model called HRCA+ to increase accuracy. The HRCA model updates the information learned from the previous relation-pair to the next relation-pair. HRCA+ utilizes the textual information and the interior relationship between every two parts in a passage, a question, and the corresponding candidate options. Our proposed method outperforms other state-of-the-art methods. On the Semeval-2018 Task 11 dataset, our proposed method improved accuracy levels from 95.8% to 97.2%, and on the DREAM dataset, it improved accuracy levels from 90.4% to 91.6% without extra training data, from 91.8% to 92.6% with extra training data.

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme.

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required by the anomaly detection framework are stored to enable arbitrary arithmetic calculations over FHE. Furthermore, we adopt a private information retrieval (PIR) approach with FHE to enable approximate search with LUTs, which reduces the execution time of the attack detection service while protecting private information. Besides, we show that by adjusting the significant digits of inputs and outputs in our LUT, we can control the detection accuracy and execution time of the attack detection, even while using FHE. Our experiments confirmed that our proposed method is able to detect the injection of false power consumption in the range of 11–17 secs of execution time, depending on detection accuracy.

Overfitting reduces the generalizability of deep neural networks (DNNs). Overfitting is generally detected by comparing the accuracies and losses of training and validation data; however, the detection method requires vast amounts of training data and is not always effective for forthcoming data due to the heterogeneity between training and forthcoming data. The dropout technique has been employed to prevent DNNs from overfitting, where the neurons in DNNs are invalidated randomly during their training. It has been hypothesized that this technique prevents DNNs from overfitting by restraining the co-adaptions among neurons. This hypothesis implies that overfitting of a DNN is a result of the co-adaptions among neurons and can be detected by investigating the inner representation of DNNs. Thus, we propose a method to detect overfitting of DNNs using no training and test data. The proposed method measures the degree of co-adaptions among neurons using persistent homology (PH). The proposed PH-based overfitting measure (PHOM) method constructs clique complexes on DNNs using the trained parameters of DNNs, and the one-dimensional PH investigates the co-adaptions among neurons. Thus, PHOM requires no training and test data to measure overfitting. We applied PHOM to convolutional neural networks trained for the classification problems of the CIFAR-10, SVHN, and Tiny ImageNet data sets. The experimental results demonstrate that PHOM reveals the degree of overfitting of DNNs to the training data, which suggests that PHOM enables us to filter overfitted DNNs without requiring the training and test data.

The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.

In the big data era, cloud-based machine learning as a service (MLaaS) has attracted considerable attention. However, when handling sensitive data, such as financial and medical data, a privacy issue emerges, because the cloud server can access clients' raw data. A common method of handling sensitive data in the cloud uses homomorphic encryption, which allows computation over encrypted data without decryption. Previous research adopted a low-degree polynomial mapping function, such as the square function, for data classification. However, this technique results in low classification accuracy. This study seeks to improve the classification accuracy for inference processing in a convolutional neural network (CNN) while using homomorphic encryption. We apply various orders of the polynomial approximations of Google's Swish and ReLU activation functions. We also adopt batch normalization to normalize the inputs for the approximated activation functions to fit the input range to minimize the error. We implemented CNN inference labeling over homomorphic encryption using the Microsoft's Simple Encrypted Arithmetic Library (SEAL) for the Cheon-Kim-Kim-Song (CKKS) scheme. The experimental evaluations confirmed classification accuracies of 99.29% and 81.06% for MNIST and CIFAR-10, respectively, which entails 0.11% and 4.69% improvements, respectively, over previous methods.

Online advertising is widely used by commercial companies to attract customers. Tuning advertisement delivery to achieve a high conversion rate (CVR) is crucial for improving advertising effectiveness. Because advertisers require demandside platforms (DSPs) to deliver a certain number of ads within a fixed period, it is challenging to maximize CVR while satisfying ads delivery constraints. Such a combinatorial optimization problem is NP-hard when we have a considerable number of both ads and users. In this paper, we adopt Digital Annealer (DA), a quantum-inspired Ising computer, to solve the combinatorial optimization problem. The experimental evaluation result shows that the proposed method increases accuracy from 0.176 to 0.326 and achieves 20.8 times speed-up compared to baseline.

The Smart Systems and Services Innovative Professional Education (Smart SE) program is a certification program developed as part of the education network for the Practical information Technologies (enPiT-Pro) project, which is funded by the Japan Ministry of Education, Culture, Sports, Science and Technology. The Smart SE program provides industry professionals working in fields related to information and communication technology (ICT) with additional training and education in smart systems and services that utilize various technologies such as IoT, Cloud, Big Data, and Artificial Intelligence (AI) for businesses. Here, we illustrate its purpose, curriculum and features to respond to the needs of industrial professional education.

Smart computing aims to improve the quality of life by utilizing Internet-of-Things devices and cloud computing. Typically, this computing handles private and/or personal information so concealing such sensitive information is a challenge. Adopting fully homomorphic encryption (FHE) is one approach for handling such sensitive information safely; that is, we can calculate the encrypted data without decryption. However, the time and space complexity of the FHE operation is high. Thus, its computation takes a long time. In this study, we aim to shorten FHE execution time by adopting our new scheduling algorithm, which divides a task into several macro-tasks and then assigns a set of threads. We assume a cloud computing system that is equipped with a many-core CPU. Thus, we propose the dynamic allocation method of computation resource to macro-tasks (DAMCREM), which dynamically allocates a certain number of threads (selected from pre-defined candidates) to each macro-task of every given job. In the evaluation, we compared DAMCREM to naive methods that allocate a pre-defined number of threads to each macro-task. The result shows that the average latency and maximum latency of job execution is less than those of naive methods, even when the average interval of job arrival is short.

In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users’ frequently visited areas, because users are likely to revisit such areas. In addition to the (POI) category diversity that is a popular diversification objective in recommendation domains, diversification of recommended POI locations is an interesting subject itself. Despite its importance, existing POI recommender studies generally focus on and evaluate prediction accuracy. In this article, geographical diversification ( geo-diversification ), a novel diversification concept that aims to increase recommendation coverage for a target users’ geographic areas of interest, is introduced, from which a method that improves geo-diversity as an addition to existing state-of-the-art POI recommenders is proposed. In experiments with the datasets from two real Location Based Social Networks (LSBNs), we first analyze the performance of four state-of-the-art POI recommenders from various evaluation perspectives including category diversity and geo-diversity that have not been examined previously. The proposed method consistently improves geo-diversity (CPR(geo)@20) by 5 to 12% when combined with four state-of-the-art POI recommenders with negligible prediction accuracy (Recall@20) loss and provides 6 to 18% geo-diversity improvement with tolerable prediction accuracy loss (up to 2.4%).

Unreliable web pages, such as fake news, have become an unavoidable problem. To tackle this problem, recent researches have adopted both content and social features to predict the credibility of the web pages; however, the accuracy is almost saturated. In this paper, we propose the adoption of Google Lighthouse features to predict web page credibility. Our experimental results show that the proposed method achieves an increased accuracy of 7.9% in comparison with state-of-the-art methods.

Phishing is a type of personal information theft in which phishers lure users to steal sensitive information. Phishing detection mechanisms using various techniques have been developed. Our hypothesis is that phishers create fake websites with as little information as possible in a webpage, which makes it difficult for content- and visual similarity-based detections by analyzing the webpage content. To overcome this, we focus on the use of Uniform Resource Locators (URLs) to detect phishing. Since previous work extracts specific special-character features, we assume that non-alphanumeric (NAN) character distributions highly impact the performance of URL-based detection. We hence propose a new feature called the entropy of NAN characters for URL-based phishing detection. Experimental evaluation with balanced and imbalanced datasets shows 96% ROC AUC on the balanced dataset and 89% ROC AUC on the imbalanced dataset, which increases the ROC AUC as 5 to 6% from without adopting our proposed feature.

The preservation of privacy during a search has become a serious problem in recent years. There is an increasing requirement to ensure that user queries are not abused by a third party, including the search provider. Fully homomorphic encryption (FHE) can conduct addition and multiplication directly over ciphertext. Using FHE, privacy, concerning both the user queries and the database of the search provider, can be protected. In this paper, we propose a privacy-preserving query system model. We implemented the proposed model in a real-world medicine side-effect query system. We applied a filtering technique, prior to the query deployment, to reduce the size of the database and used multi-threading to accelerate the search. The system was tested 10,000 times with a random query, using a database comprising 40,000 records of simulation data, and completed 99.84% of the queries within 60 seconds (s), proving the real-world applicability of our system.

In the era of big data and cloud computing, outsourcing data storage to the cloud poses the risk of its abuse or leakage. Thus, we address the problem of delegating computation on outsourced private datasets while maintaining privacy. In this study, we consider a scenario involving two data owners outsourcing their datasets to a cloud service. The cloud performs a set union computation, after which the querier sends a query to obtain information from both datasets. We propose a protocol that uses fully homomorphic encryption (FHE) and Cartesian-join of Bloom filters (CBF) as proposed by Wang et al. The protocol obtains information on the existence of a particular set of elements without learning about the residing source. To the best of our knowledge, our protocol, by using the FHE and CBF matrix, is a novel approach to ensuring the security of outsourced set union operations.

Machine learning classification has a wide range of applications. In the big data era, a client may want to outsource classification tasks to reduce the computational burden at the client. Meanwhile, an entity may want to provide a classification model and classification services to such clients. However, applications such as medical diagnosis require sensitive data that both parties may not want to reveal. Fully homomorphic encryption (FHE) enables secure computation over encrypted data without decryption. By applying FHE, classification can be outsourced to a cloud without revealing any data. However, existing studies on classification over FHE do not achieve the scenario of outsourcing classification to a cloud while preserving the privacy of the classification model, client's data and result. In this work, we apply FHE to a naïve Bayes classifier and, to the best of our knowledge, propose the first concrete secure classification protocol that satisfies the above scenario.

Smart grids are indispensable applications in smart connected communities (SCC). To construct privacy-preserving anomaly detection systems on a smart grid, we adopt fully homomorphic encryption (FHE) to protect users' sensitive data. Although FHE allows a third party to perform calculations on encrypted data without decryption, FHE only supports addition and multiplication on encrypted data. In anomaly detection, we must calculate both harmonic and arithmetic means consisting of logarithms. A naïve implementation of such arithmetic operations with FHE is a bitwise operation; thus, it requires huge computation time. To speed up such calculations, we propose an efficient protocol to evaluate any functions with FHE using a lookup table (LUT). Our protocol allows integer encoding, i.e., a set of integers is encrypted as a single ciphertext, rather than using bitwise encoding. Our experimental results in a multi-threaded environment show that the runtime of our protocol is approximately 51 s when the size of the LUT is 448,000. Our protocol is more practical than the previously proposed bitwise implementation.

Recommender systems have been used for analyzing users' preference through their past activities and recommend items in which they might be interested in. There are numerous research on improving the accuracy of recommendation being conducted, so the recommender system reads user preference more accurately. However, it is important to consider the recommendation diversity, because lacking diversity will lead to recommendation being repetitive and obvious. In this paper, we propose a method to re-rank the recommendation list by appearance frequency of items to recommend more range of items. The experimental result shows that our method consistently performs better than a related work to improve recommendation diversity.

Unreliable web pages such as fake news has become a global problem in big data era. The motivation to publish fake news is often for profit; for example, earning advertisement income by putting ads on their web pages. In this paper, we focus on different usage of HTML source tags between reliable and unreliable web pages, then propose new features for predicting their credibility. The experimental result shows that our proposed features increase accuracy when used together with previously proposed Contents features.

In the big data era, multithreaded processing on a many-core machine, whose core number is still increasing, has become essential to parallelize the execution of big data applications, besides distributed computing. In such a machine, malloc-intensive applications cannot scale due to lock contentions among threads, which becomes worse as the number of threads increases. To solve the problem, we propose a new method to reduce lock contentions by batch malloc, pseudo free, and fine-grained data-locking. Experimental result shows 4.72 times speed-up in comparison with JEmalloc which is the fastest memory allocator among previous ones.

In the big data era, many users upload data to cloud while security concerns are growing. By using attribute-based encryption (ABE), users can securely store data in cloud while exerting access control over it. Revocation is necessary for real-world applications of ABE so that revoked users can no longer decrypt data. In actual implementations, however, revocation requires re-encryption of data in client side through download, decrypt, encrypt, and upload, which results in huge communication cost between the client and the cloud depending on the data size. In this paper, we propose a new method where the data can be re-encrypted in cloud without downloading any data. The experimental result showed that our method reduces the communication cost by one quarter in comparison with the trivial solution where re-encryption is performed in client side.

Discovery of repeated patterns, known as motifs, from long time series is essential for providing hidden knowledge to real-world applications like medical, financial and weather analysis. Motifs can be discovered on raw time series directly or on their transformed abstract representation alternatively. Most of time series motif discovery methods require predefined motif length, which results in long execution time because we have to vary the length to discover motifs with different lengths. To solve the problem, we propose an efficient method for discovering variable length motifs in combination of approximate method with exact verification. First, symbolic representation is adopted to discover motifs roughly followed by exact examination of the found motifs with original real-valued data to achieve fast and exact discovery. The experiments show that our proposed method successfully discovered significant motifs efficiently in comparison with state-of-the-art methods: MK and SBF.

In recommending POIs(Point-Of-Interests), factors such as the diversity of the recommended POIs are as important as accuracy for providing a satisfactory recommendation. Although existing diversification methods can help POI recommender systems suggest more diverse POIs, they lack "geographical diversification," which results in the concentration of the supposedly "diverse" recommended POIs on "a small portion" in areas where the target-user is most active. This is caused by the neglect of POI locations in the diversification, i.e., existing diversification methods try to diversify the categories of recommended items. However, geographical diversification is essential for users whose activity interests comprise many sub-areas and who require a variety of recommended POIs encompassing all their activity interests. In this paper, we propose a novel proportional geographical diversification method that recommends a variety of POIs located in the activity district of a user such that the variety of sub-areas in the district is proportional to the frequency of his/her activity in each sub-area. We compare the performance of the proposed method with existing diversification methods using real datasets. The evaluation result shows that no method except the proposed one can significantly increase geographical diversity at the expense of tolerable accuracy loss.

With the rapid development of cloud storage services and IoT environment, how to securely and efficiently search without compromising privacy has been an indispensable problem. In order to address such a problem, much works have been proposed for searching over encrypted data. Motivated by storing sensitive data such as genomic and medical data, substring search for encrypted data has been studied. Previous work either leaks query access pattern using vulnerable cryptographic model or performs search over plaintext data by an encrypted query. Thus they are not compatible with outsourcing scenario where searched data is stored in encrypted form which is searched by an encrypted substring query without leaking query access pattern, i.e., private substring search. In order to perform private substring search, Fully Homomorphic Encryption (FHE) can be adopted although it induces computationally huge overhead. Because of the huge overhead, performing private substring search efficiently over FHE is a challenging task. In this work, we propose a private substring search protocol over encrypted data by adopting FHE followed by examining its feasibility. In particular, we make use of batching technique which can accelerate homomorphic computation in SIMD manner. In addition, we propose a data structure which can be useful to specific searching function for batched computation. Our experimental result showed our proposed method is feasible.

In the big data era, security and privacy concerns are growing. One of the big challenges is secure Frequent Pattern Mining (FPM) over Fully Homomorphic Encryption (FHE). There exist some research efforts aimed at speeding-up, however, we have a big room so as to decrease time and space complexity. Apriori over FHE, in particular, generates a large number of ciphertexts during the support calculation, which results in both large time and space complexity. To solve it, we proposed a speedup technique, around 430 times faster and 18.9 times smaller memory usage than the state-of-the-art method, by adopting both packing and caching mechanism. In this paper, we further propose to decrease the memory space used for caching. Our goal is to discard redundant cached ciphertexts without increasing the execution time. Our experimental results show that our method decreases the memory usage by 6.09% at most in comparison with our previous method without increasing the execution time.

Privacy-preserving string search is a crucial task for analyzing genomics-driven big data. In this work, we propose a cryptographic protocol that uses Fully Homomorphic Encryption (FHE) to enable a client to search on a genome sequence database without leaking his/her query to the server. Though FHE supports both addition and multiplication over encrypted data, random noise inside ciphertexts grows with every arithmetic operation especially multiplication, which results in incorrect decryption when the noise amount exceeds its threshold called level. There are two approaches to avoid the incorrect decryption: one is setting the sufficient level that assures correct decryption within the limited number of operations, and the other is resetting the noise by the method called bootstrapping. It is important to find an optimal balance between overhead caused by the level and overhead caused by the bootstrapping, since using higher level deteriorates the performance of all the arithmetic operations, while the more number of bootstrappings causes more expensive overhead. In this study, we propose an efficient approach to minimize the number of bootstrappings while reducing the level as much as possible. Our experimental result shows that it runs at most 10 times faster than a naïve approach.

The challenges in efficient data representation and similarity measures on massive amounts of time series have enormous impact on many applications. This paper addresses an improvement on Symbolic Aggregate approXimation (SAX), is one of the efficient representations for time series mining. Because SAX represents its symbols by the average (mean) value of a segment with the assumption of Gaussian distribution, it is insufficient to serve the entire deterministic information and causes sometimes incorrect results in time series classification. In this work, SAX representation and distance measure is improved with the addition of another moment of the prior distribution, standard deviation; SAX_SD is proposed. We provide comprehensive analysis for the proposed SAX_SD and confirm both the highest classification accuracy and the highest dimensionality reduction ratio on University of California, Riverside (UCR) datasets in comparison to state of the art methods such as SAX, Extended SAX (ESAX) and SAX Trend Distance (SAX_TD).

Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.

Human mobility analysis with Location-Based Social Network (LBSN) data is the basis of personalized point-of-interest (POI) recommendations or location-aware advertisements. In addition to personal preference and spatiotemporal factors such as time and distance, personal context has a strong influence on mobility. An individual's familiarity with an area is an interesting context because it can bias the influence of certain factors. For example, the mobility patterns of two persons who have similar preferences are different when their familiarity with the area is different, even in the same area. In this paper, we analyze familiarity's effect on mobility patterns by using over 1.4 million check-ins gathered from Foursquare. The analysis indicates that there is a skewness of the visit time and visited venue distribution in unfamiliar areas. For instance, people go to unfamiliar areas on weekends; and venues for cultural experiences, such as museums, strongly contribute to the motivation of visit.

Internet security issues require authorship identification for all kinds of internet contents; however, authorship identification for microblog users is much harder than other documents because microblog texts are too short. Moreover, when the number of candidates becomes large, i.e., big data, it will take long time to identify. Our proposed method solves these problems. The experimental results show that our method successfully identifies the authorship with 53.2% of precision out of 10,000 microblog users in the almost half execution time of previous method.

We propose intensity constraint-based closed sequential pattern mining algorithm, called IC-BIDE, for a coding pattern extraction. Source code often contains frequent patterns of function calls or control flows, i.e., "coding patterns." Previous studies used sequential pattern mining to extract coding pattern, however, these algorithms have not been optimized for coding pattern extraction, which results in useless patterns as well as long execution times. We propose a new constraint, called "intensity constraint," in order to enhance closed sequential pattern mining and efficiently extract coding patterns. Our proposed algorithm is based on BI-Directional Execution (BIDE), an algorithm proposed expressly for closed sequential pattern mining. BIDE algorithm is not able to adapt to constraint-based closed sequential pattern mining. We extend BIDE algorithm and prove that our extended algorithm is able to adapt to intensity constraint-based closed sequential pattern mining. Our contributions are as follow, 1) We propose a new constraint, which we call "intensity", 2) We propose intensity constraint-based closed sequential pattern mining algorithm, which we call "IC-BIDE" algorithm. Experimental results with open source software (Bullet Physics, MySQL, and OpenCV) show that IC-BIDE algorithm successfully excludes useless pattern effectively. Moreover, our proposed method is able to accelerate the extraction by a factor of 8.9 in comparison with the BIDE algorithm.

BACKGROUND: Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design. RESULTS: Here, we propose a novel method for identifying MoRFs, named as MFSPSSMpred (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor). Firstly, a masking method is used to calculate the average local conservation scores of residues within a masking-window length in the position-specific scoring matrix (PSSM). Then, the scores below the average are filtered out. Finally, a smoothing method is used to incorporate the features of flanking regions for each residue to prepare the feature sets for prediction. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the PSSM of sequence only. Experimental results show that, comparing with other methods tested on the same datasets, our method achieves the best performance: achieving 0.004~0.079 higher AUC than other methods when tested on TEST419, and achieving 0.045~0.212 higher AUC than other methods when tested on TEST2012. In addition, when tested on an independent membrane proteins-related dataset, MFSPSSMpred significantly outperformed the existing predictor MoRFpred. CONCLUSIONS: This study suggests that: 1) amino acid composition and physicochemical properties in the flanking regions of MoRFs are very different from those in the general non-MoRF regions; 2) MoRFs contain both highly conserved residues and highly variable residues and, on the whole, are highly locally conserved; and 3) combining contextual information with local conservation information of residues facilitates the prediction of MoRFs.

In recent years, digital notebooks have been replacing traditional paper-based notebooks with the development of handwriting input devices. Currently, we can access digital notebooks in various devices, including mobile devices. When we use such mobile devices, however, their limited screen size results in difficulty in understanding the summary of hand-written documents, without the use of a zoom feature. In this paper, we therefore propose the "Legible Thumbnail" that helps us to understand the summary without zooming. Our method detects the important words based on emphasis, such as an underline, and the method outputs the emphasized words to the thumbnail. Experiments show our thumbnail reduces search time by 21%.

In this paper, we propose a non-blocking buffer management scheme based on a lock-free variant of the GCLOCK page replacement algorithm. Concurrent access to the buffer management module is a major factor that prevents database scalability to processors. Therefore, we propose a non-blocking scheme for bufferfix operations that fix buffer frames for requested pages without locks by combining Nb-GCLOCK and a non-blocking hash table. Our experimental results revealed that our scheme can obtain nearly linear scalability to processors up to 64 processors, although the existing locking-based schemes do not scale beyond 16 processors.

Inputting mathematical expressions with a mouse and a keyboard is a troublesome task. Thus, a number of mathematical expression recognition systems capable of recognizing handwritten mathematical expressions to input them into computers have been proposed. Even with these systems, however, structure recognition of mathematical expressions is still difficult. This paper presents MathBox, a new pen-based interface for inputting mathematical expressions into computers. MathBox interactively shows "boxes" in which the user can write one symbol. The boxes are shown along with the user's writing. For example, when the user writes 'x,' the boxes for a power and an index of 'x' and for the next symbol are shown. When the user inputs a fraction line, boxes for the numerator, denominator, and the next symbol are shown. MathBox skips recognizing the structures of expressions, which enables users to write mathematical expressions with practical accuracy.

In this paper, we propose a new system extracting potentially copyright infringement texts from the Web, called EPCI. EPCI extracts them in the following way: (1) generating a set of queries based on a given copyright reserved seed-text, (2) putting every query to search engine API, (3) gathering the search result Web pages from high ranking until the similarity between the given seed-text and the search result pages becomes less than a given threshold value, and (4) merging all the gathered pages, then re-ranking them in the order of their similarity. Our experimental result using 40 seed-texts shows that EPCI is able to extract 132 potentially copyright infringement Web pages per a given copyright reserved seed-text with 94% precision in average.

BACKGROUND: Multiple sequence alignment (MSA) is a useful tool in bioinformatics. Although many MSA algorithms have been developed, there is still room for improvement in accuracy and speed. In the alignment of a family of protein sequences, global MSA algorithms perform better than local ones in many cases, while local ones perform better than global ones when some sequences have long insertions or deletions (indels) relative to others. Many recent leading MSA algorithms have incorporated pairwise alignment information obtained from a mixture of sources into their scoring system to improve accuracy of alignment containing long indels. RESULTS: We propose a novel group-to-group sequence alignment algorithm that uses a piecewise linear gap cost. We developed a program called PRIME, which employs our proposed algorithm to optimize the well-defined sum-of-pairs score. PRIME stands for Profile-based Randomized Iteration MEthod. We evaluated PRIME and some recent MSA programs using BAliBASE version 3.0 and PREFAB version 4.0 benchmarks. The results of benchmark tests showed that PRIME can construct accurate alignments comparable to the most accurate programs currently available, including L-INS-i of MAFFT, ProbCons, and T-Coffee. CONCLUSION: PRIME enables users to construct accurate alignments without having to employ pairwise alignment information. PRIME is available at http://prime.cbrc.jp/.

No abstract available.

No abstract available.

Article Fine-grain multithreading with the EM-X multiprocessor Share on Authors: Andrew Sohn Computer and Information Science Dept., New Jersey Institute of Technology, Newark, NJ Computer and Information Science Dept., New Jersey Institute of Technology, Newark, NJView Profile , Yuetsu Kodama Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, Japan Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, JapanView Profile , Jui Ku Computer and Information Science Dept., New Jersey Institute of Technology, Newark, NJ Computer and Information Science Dept., New Jersey Institute of Technology, Newark, NJView Profile , Mitsuhisa Sato Real World Computing Tsukuba Research Center, Tsukuba, Ibaraki, 305, Japan Real World Computing Tsukuba Research Center, Tsukuba, Ibaraki, 305, JapanView Profile , Hirofumi Sakane Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, Japan Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, JapanView Profile , Hayato Yamana Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, Japan Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, JapanView Profile , Shuichi Sakai Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, Japan Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, JapanView Profile , Yoshinori Yamaguchi Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, Japan Computer Architecture Section, Electrotechnical Laboratory, Tsukuba-shi, Ibaraki 305, JapanView Profile Authors Info & Claims SPAA '97: Proceedings of the ninth annual ACM symposium on Parallel algorithms and architecturesJune 1997 Pages 189–198https://doi.org/10.1145/258492.258511Online:01 June 1997Publication History 7citation121DownloadsMetricsTotal Citations7Total Downloads121Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The purpose of this paper is to propose a new fast EM-4 multiprocessor to decrease the control overhead of macrotasks.Preliminary evaluations show that the control overhead of the proposed scheme is smaller than that of the other control schemes.Moreover, it is confirmed that the distributed control can be implemented by using software when the average macrotssk execution time is larger than 14.4ps on the EM-4 multiprocessor.

Latency tolerance is essential in achieving high performance on parallel computers for remote function calls and fine-grained remote memory accesses. EM-X supports interprocessor communication on an execution pipeline with small and simple packets. It can create a packet in one cycle, and receive a packet from the network in the on-chip buffer without interruption. EM-X invokes threads on packet arrival, minimizing the overhead of thread switching. It can tolerate communication latency by using efficient multi-threading and optimizing packet flow of fine grain communication. EM-X also supports the synchronization of two operands, direct remote memory read/write operations and flexible packet scheduling with priority. This paper describes distinctive features of the EM-X architecture and reports the performance of small synthetic programs and larger more realistic programs.

The purpose of this paper is to propose and evaluate a new scheme, called the Preceding Activation Scheme with Graph Unfolding, which translates a FORTRAN program into a dataflow graph and executes it efficiently. The problems in restructuring a FORTRAN program into a dataflow graph are that (1) a FORTRAN program is not written in a single assignment rule and (2) it has an explicit control flow. These problems result in little parallelism because many gate-operations, such as T/F gates, are introduced in the dataflow graph to synchronize the data movement. Therefore, discarding these gate-operations is the key to exposing parallelism in a FORTRAN program. In this paper, the preceding activation scheme with graph unfolding is proposed to discard these gate-operations. Then, the result of the performance evaluation by the -Harray- software simulator is presented. It is shown that the execution speed with the proposed scheme for flow graphs without backward branches is about 1.5 times as fast as that with the extended activation scheme which initiates the execution only after it is confirmed that a basic block will be selected at a conditional branch. Moreover, the execution speed is 2.7 times as fast as that with the extended activation scheme if a flow graph including backward branches are unfolded by the proposed scheme.

This paper proposes a parallel processing system -Harray- for scientific computations. Data flow computers are expected to obtain the high performance because they can extract parallelism fully from a program. However, they have many problems, such as the difficulty of controlling the sequence of execution. The -Harray- system is an array processor which adapts two levels of control mechanism; data flow execution in each processor and control flow between processors, in order to take full advantage of both mechanisms. A task which is assigned to a processor is called a “macro-block”. Three types of macro-blocking and three types of activation schemes for the macro-block which initiates its execution are introduced in order to attain the high performance. Moreover, a hardware synchronization mechanism is used to reduce synchronization overhead and to gain the liner speedup of the -Harray- system.