Journal of Information and Intelligence最新文献

{"title":"KOGPSR: A 3D GPSR algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna","authors":"Zhongjie Wang,&nbsp;Yuhan Ruan,&nbsp;Yongzhao Li,&nbsp;Tao Li,&nbsp;Rui Zhang,&nbsp;Jian Liang","doi":"10.1016/j.jiixd.2023.11.001","DOIUrl":"10.1016/j.jiixd.2023.11.001","url":null,"abstract":"<div><p>Nowadays, flying ad hoc network (FANET) has captured great attention for its huge potential in military and civilian applications. However, the high-speed movement of unmanned aerial vehicles (UAVs) in three-dimensional (3D) space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms. To improve the performance of packet transmission in the 3D high dynamic FANETs, we propose a 3D greedy perimeter stateless routing (GPSR) algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna (KOGPSR). Especially, in data forwarding part of the KOGPSR, we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs, and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing. In addition, in order to enhance the accuracy of the location information of high dynamic UAVs, we design an adaptive Kalman algorithm to track and predict the motion of UAVs. Finally, a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm. The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 3","pages":"Pages 191-208"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000719/pdfft?md5=2b18cbef163ce52128b820ccdbf4100b&pid=1-s2.0-S2949715923000719-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DI-VTR: Dual inter-modal interaction model for video-text retrieval","authors":"","doi":"10.1016/j.jiixd.2024.03.003","DOIUrl":"10.1016/j.jiixd.2024.03.003","url":null,"abstract":"<div><p>Video-text retrieval is a challenging task for multimodal information processing due to the semantic gap between different modalities. However, most existing methods do not fully mine the intra-modal interactions, as with the temporal correlation of video frames, which results in poor matching performance. Additionally, the imbalanced semantic information between videos and texts also leads to difficulty in the alignment of the two modalities. To this end, we propose a dual inter-modal interaction network for video-text retrieval, i.e., DI-VTR. To learn the intra-modal interaction of video frames, we design a contextual-related video encoder to obtain more fine-grained content-oriented video representations. We also propose a dual inter-modal interaction module to accomplish accurate multilingual alignment between the video and text modalities by introducing multilingual text to improve the representation ability of text semantic features. Extensive experimental results on commonly-used video-text retrieval datasets, including MSR-VTT, MSVD and VATEX, show that the proposed method achieves significantly improved performance compared with state-of-the-art methods.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 5","pages":"Pages 388-403"},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294971592400026X/pdfft?md5=99a5f02c39ebbf60a2f3d5a6ebd243c0&pid=1-s2.0-S294971592400026X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140268908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on membership inference attacks and defenses in machine learning","authors":"","doi":"10.1016/j.jiixd.2024.02.001","DOIUrl":"10.1016/j.jiixd.2024.02.001","url":null,"abstract":"<div><p>Membership inference (MI) attacks mainly aim to infer whether a data record was used to train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a tremendous amount of attention in the research community. One existing work conducted — to our best knowledge — the first dedicated survey study in this specific area: The survey provides a comprehensive review of the literature during the period of 2017∼2021 (e.g., over 100 papers). However, due to the tremendous amount of progress (i.e., 176 papers) made in this area since 2021, the survey conducted by the one existing work has unfortunately already become very limited in the following two aspects: (1) Although the entire literature from 2017∼2021 covers 18 ways to categorize (all the proposed) MI attacks, the literature during the period of 2017∼2021, which was reviewed in the one existing work, only covered 5 ways to categorize MI attacks. With 13 ways missing, the survey conducted by the one existing work only covers 27% of the landscape (in terms of how to categorize MI attacks) if a retrospective view is taken. (2) Since the literature during the period of 2017∼2021 only covers 27% of the landscape (in terms of how to categorize), the number of new insights (i.e., why an MI attack could succeed) behind all the proposed MI attacks has been significantly increasing since year 2021. As a result, although none of the previous work has made the insights as a main focus of their studies, we found that the various insights leveraged in the literature can be broken down into 10 groups. Without making the insights as a main focus, a survey study could fail to help researchers gain adequate intellectual depth in this area of research. In this work, we conduct a systematic study to address these limitations. In particular, in order to address the first limitation, we make the 13 newly emerged ways to categorize MI attacks as a main focus on the study. In order to address the second limitation, we provide — to our best knowledge — the first review of the various insights leveraged in the entire literature. We found that the various insights leveraged in the literature can be broken down into 10 groups. Moreover, our survey also provides a comprehensive review of the existing defenses against MI attacks, the existing applications of MI attacks, the widely used datasets (e.g., 107 new datasets), and the evaluation metrics (e.g., 20 new evaluation metrics).</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 5","pages":"Pages 404-454"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000064/pdfft?md5=a51bb65bff0598f8d5676e4453da8337&pid=1-s2.0-S2949715924000064-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight hardware implementation of CRYSTALS-Kyber","authors":"Shiyang He ,&nbsp;Hui Li ,&nbsp;Fenghua Li ,&nbsp;Ruhui Ma","doi":"10.1016/j.jiixd.2024.02.004","DOIUrl":"10.1016/j.jiixd.2024.02.004","url":null,"abstract":"<div><p>The security of cryptographic algorithms based on integer factorization and discrete logarithm will be threatened by quantum computers in future. Since December 2016, the National Institute of Standards and Technology (NIST) has begun to solicit post-quantum cryptographic (PQC) algorithms worldwide. CRYSTALS-Kyber was selected as the standard of PQC algorithm after 3 rounds of evaluation. Meanwhile considering the large resource consumption of current implementation, this paper presents a lightweight architecture for ASICs and its implementation on FPGAs for prototyping. In this implementation, a novel compact modular multiplication unit (MMU) and compression/decompression module is proposed to save hardware resources. We put forward a specially optimized schoolbook polynomial multiplication (SPM) instead of number theoretic transform (NTT) core for polynomial multiplication, which can reduce about 74% SLICE cost. We also use signed number representation to save memory resources. In addition, we optimize the hardware implementation of the Hash module, which cuts off about 48% of FF consumption by register reuse technology. Our design can be implemented on Kintex-7 (XC7K325T-2FFG900I) FPGA for prototyping, which occupations of 4777/4993 LUTs, 2661/2765 FFs, 1395/1452 SLICEs, 2.5/2.5 BRAMs, and 0/0 DSP respective of client/server side. The maximum clock frequency can reach at 244 ​MHz. As far as we know, our design consumes the least resources compared with other existing designs, which is very friendly to resource-constrained devices.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 167-176"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294971592400009X/pdfft?md5=554b4ca1fa191ff4a92f726744e62d79&pid=1-s2.0-S294971592400009X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140465321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling security, privacy, and ethical concerns of ChatGPT","authors":"Xiaodong Wu,&nbsp;Ran Duan,&nbsp;Jianbing Ni","doi":"10.1016/j.jiixd.2023.10.007","DOIUrl":"10.1016/j.jiixd.2023.10.007","url":null,"abstract":"<div><p>This paper delves into the realm of ChatGPT, an AI-powered chatbot that utilizes topic modeling and reinforcement learning to generate natural responses. Although ChatGPT holds immense promise across various industries, such as customer service, education, mental health treatment, personal productivity, and content creation, it is essential to address its security, privacy, and ethical implications. By exploring the upgrade path from GPT-1 to GPT-4, discussing the model's features, limitations, and potential applications, this study aims to shed light on the potential risks of integrating ChatGPT into our daily lives. Focusing on security, privacy, and ethics issues, we highlight the challenges these concerns pose for widespread adoption. Finally, we analyze the open problems in these areas, calling for concerted efforts to ensure the development of secure and ethically sound large language models.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 102-115"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000707/pdfft?md5=d54b43065c82b1dd4241ba7d67e27d46&pid=1-s2.0-S2949715923000707-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BloomDT - An improved privacy-preserving decision tree inference scheme","authors":"Sean Lalla,&nbsp;Rongxing Lu,&nbsp;Yunguo Guan,&nbsp;Songnian Zhang","doi":"10.1016/j.jiixd.2024.02.003","DOIUrl":"10.1016/j.jiixd.2024.02.003","url":null,"abstract":"<div><p>Outsourcing decision tree models to cloud servers can allow model providers to distribute their models at scale without purchasing dedicated hardware for model hosting. However, model providers may be forced to disclose private model details when hosting their models in the cloud. Due to the time and monetary investments associated with model training, model providers may be reluctant to host their models in the cloud due to these privacy concerns. Furthermore, clients may be reluctant to use these outsourced models because their private queries or their results may be disclosed to the cloud servers. In this paper, we propose BloomDT, a privacy-preserving scheme for decision tree inference, which uses Bloom filters to hide the original decision tree's structure, the threshold values of each node, and the order in which features are tested while maintaining reliable classification results that are secure even if the cloud servers collude. Our scheme's security and performance are verified through rigorous testing and analysis.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 130-147"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000088/pdfft?md5=7d9b7fbb49ca778f809e1f16a75c50b6&pid=1-s2.0-S2949715924000088-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical and privacy-preserving geo-social-based POI recommendation","authors":"Qi Xu ,&nbsp;Hui Zhu ,&nbsp;Yandong Zheng ,&nbsp;Fengwei Wang ,&nbsp;Le Gao","doi":"10.1016/j.jiixd.2024.01.001","DOIUrl":"10.1016/j.jiixd.2024.01.001","url":null,"abstract":"<div><p>With the rapid development of location-based services and online social networks, POI recommendation services considering geographic and social factors have received extensive attention. Meanwhile, the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services. However, there is a degree of distrust of the cloud by service providers. To protect digital assets, service providers encrypt data before outsourcing it. However, encryption reduces data availability, making it more challenging to provide POI recommendation services in outsourcing scenarios. Some privacy-preserving schemes for geo-social-based POI recommendation have been presented, but they have some limitations in supporting group query, considering both geographic and social factors, and query accuracy, making these schemes impractical. To solve this issue, we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users, which are named GSPR-S and GSPR-G. Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. Then, we apply BGV fully homomorphic encryption to design some private algorithms, including a private max/min operation algorithm, a private rectangular set operation algorithm, and a private rectangular overlapping detection algorithm. After that, we use these algorithms as building blocks in our schemes for efficiency improvement. According to security analysis, our schemes are proven to be secure against the honest-but-curious cloud servers, and experimental results show that our schemes have good performance.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 148-166"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000015/pdfft?md5=a2e0865bfbb9a59bb240fc8da82554c1&pid=1-s2.0-S2949715924000015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voice Fence Wall: User-optional voice privacy transmission","authors":"Li Luo,&nbsp;Yining Liu","doi":"10.1016/j.jiixd.2023.12.002","DOIUrl":"10.1016/j.jiixd.2023.12.002","url":null,"abstract":"<div><p>Sensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. In the past, traditional feature extraction obtains and encrypts voice features that are then transmitted to upstream servers. In order to avoid sensitive attribute disclosure, it is necessary to separate the sensitive attributes from non-sensitive attributes of voice data. Motivated by this, user-optional privacy transmission framework for voice data (called: Voice Fence Wall) is proposed. Firstly, we provide user-optional, which means users can choose the attributes (sensitive attributes) they want to be protected. Secondly, Voice Fence Wall utilizes minimum mutual information (MI) to reduce the correlation between sensitive and non-sensitive attributes, thereby separating these attributes. Finally, only the separated non-sensitive attributes are transmitted to the upstream server, the quality of voice services is satisfied without leaking sensitive attributes. To verify the reliability and practicability, three voice datasets are used to evaluate the model, the experiments demonstrate that Voice Fence Wall not only effectively separates attributes to resist attribute inference attacks, but also outperforms related work in terms of classification performance. Specifically, our framework achieves 89.84 ​% accuracy in sentiment recognition and 6.01 ​% equal error rate in voice authentication.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 116-129"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294971592300080X/pdfft?md5=7d514122810a42466002016ad09b7381&pid=1-s2.0-S294971592300080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139393204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hyperspectral unmixing approach for ink mismatch detection in unbalanced clusters","authors":"Faryal Aurooj Nasir ,&nbsp;Salman Liaquat ,&nbsp;Khurram Khurshid ,&nbsp;Nor Muzlifah Mahyuddin","doi":"10.1016/j.jiixd.2024.01.004","DOIUrl":"10.1016/j.jiixd.2024.01.004","url":null,"abstract":"<div><p>Detecting ink mismatch is a significant challenge in verifying the authenticity of documents, especially when dealing with uneven ink distribution. Conventional imaging methods frequently fail to distinguish visually similar inks. Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters. The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models (GMMs) to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely. For a more accurate estimation of quantity, which is generally not an attribute of clustering methods, we employed entropy calculations in the red, green, and blue depth channels for precise abundance estimation of ink. This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets. We evaluate our approach on the iVision handwritten hyperspectral images dataset (iVision HHID), which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images (WIHSI) database in size and diversity. This study has accomplished the unmixing task with three main challenges: unmixing of diverse ink spectral signatures (149 spectral bands instead of 33 bands in the previous dataset), without using prior knowledge and assumptions about the number of inks used in the questioned document, and not requiring large training data for performing unmixing. Furthermore, the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum. Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents, thereby enhancing the robustness of our method. The code for conducting this research can be accessed at <span>GitHub repository</span><svg><path></path></svg>.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 177-190"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715924000040/pdfft?md5=3d98b093a0be134b496feff3d3fa509c&pid=1-s2.0-S2949715924000040-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data security and privacy computing in artificial intelligence","authors":"Dengguo Feng,&nbsp;Hui Li,&nbsp;Rongxing Lu,&nbsp;Zheli Liu,&nbsp;Jianbing Ni,&nbsp;Hui Zhu","doi":"10.1016/j.jiixd.2024.02.007","DOIUrl":"https://doi.org/10.1016/j.jiixd.2024.02.007","url":null,"abstract":"","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 2","pages":"Pages 99-101"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294971592400012X/pdfft?md5=b365b0de34c8f2cd89fb4535c7790036&pid=1-s2.0-S294971592400012X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}