Ignacio Marco-Pérez, Beatriz Pérez, Angel Luis Rubio Garcia, María A. Zapata
Today, our data is not only stored on personal computers, but is managed by many devices, from cell phones or watches to smart TVs, and stored in remote repositories (usually referred to as “the cloud”). In this new context, defining what exactly “data deletion” is becomes a challenge, especially considering the many different scenarios in which it is becoming more increasingly important. This is the case, for example, of the “right to be forgotten” established by regulations such as the European General Data Protection Regulation (GDPR) or the deletion of data used as a source to feed machine learning processes, the long-term effects of which are very difficult to estimate. This work reviews the various terminology used when dealing with data deletion and analyzes the different fields and technologies to which it is related. We conclude by offering a structured discussion of key takeaways, lessons learned, and future research directions.
{"title":"The Many Faces of Data Deletion: On the Significance and Implications of Deleting Data","authors":"Ignacio Marco-Pérez, Beatriz Pérez, Angel Luis Rubio Garcia, María A. Zapata","doi":"10.1145/3779299","DOIUrl":"https://doi.org/10.1145/3779299","url":null,"abstract":"Today, our data is not only stored on personal computers, but is managed by many devices, from cell phones or watches to smart TVs, and stored in remote repositories (usually referred to as “the cloud”). In this new context, defining what exactly “data deletion” is becomes a challenge, especially considering the many different scenarios in which it is becoming more increasingly important. This is the case, for example, of the “right to be forgotten” established by regulations such as the European General Data Protection Regulation (GDPR) or the deletion of data used as a source to feed machine learning processes, the long-term effects of which are very difficult to estimate. This work reviews the various terminology used when dealing with data deletion and analyzes the different fields and technologies to which it is related. We conclude by offering a structured discussion of key takeaways, lessons learned, and future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peigen Ye, Huali Ren, Zhengdao Li, Anli Yan, Hongyang Yan, Shaowei Wang, Jin Li
State-of-the-art watermarking and fingerprinting techniques for Large Language Models (LLMs) are explored, with our analysis spanning a wide array of methodologies designed to protect the intellectual property of LLMs. The review of watermarking techniques is based on embedding watermarks during the training, logits generation, and token sampling phases. Meanwhile, we investigate the application of watermarking technology in multimodal LLMs and potential attacks on watermarks. Moreover, our examination of fingerprinting techniques revealed the ingenuity behind methods used to identify LLMs. We discussed the development of fingerprints based on model behavior and using deep learning models to learn thresholds for fingerprint comparison. Our survey has underscored the importance of advancing security measures for LLMs, especially in light of the increasing sophistication of adversarial attacks. As LLMs continue to play a pivotal role in advancing AI technologies, developing and refining security measures that safeguard their intellectual property and ensure their ethical deployment is imperative.
{"title":"Securing Large Language Models: A Survey of Watermarking and Fingerprinting Techniques","authors":"Peigen Ye, Huali Ren, Zhengdao Li, Anli Yan, Hongyang Yan, Shaowei Wang, Jin Li","doi":"10.1145/3773028","DOIUrl":"https://doi.org/10.1145/3773028","url":null,"abstract":"State-of-the-art watermarking and fingerprinting techniques for Large Language Models (LLMs) are explored, with our analysis spanning a wide array of methodologies designed to protect the intellectual property of LLMs. The review of watermarking techniques is based on embedding watermarks during the training, logits generation, and token sampling phases. Meanwhile, we investigate the application of watermarking technology in multimodal LLMs and potential attacks on watermarks. Moreover, our examination of fingerprinting techniques revealed the ingenuity behind methods used to identify LLMs. We discussed the development of fingerprints based on model behavior and using deep learning models to learn thresholds for fingerprint comparison. Our survey has underscored the importance of advancing security measures for LLMs, especially in light of the increasing sophistication of adversarial attacks. As LLMs continue to play a pivotal role in advancing AI technologies, developing and refining security measures that safeguard their intellectual property and ensure their ethical deployment is imperative.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"250 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rongxin Zhu, Lei Sheng, Kaitao Wu, Azzedine Boukerche, Libo Long, Qiuling Yang
The growing demand for marine exploration, environmental monitoring, and autonomous underwater operations has elevated the role of underwater image processing in both research and practical applications. However, the acquisition and transmission of underwater visual data are fundamentally constrained by the harsh aquatic environment, where factors such as limited bandwidth, strong light scattering, color distortion, and complex noise severely degrade image quality and restrict data throughput. These challenges not only hinder real-time perception and decision-making but also affect the efficiency of data-driven tasks such as mapping, object recognition, and navigation. To address these issues, a broad spectrum of underwater image processing methods has emerged, aiming to enhance visual clarity, compress data for efficient transmission, restore degraded signals, and enable accurate scene understanding. This survey provides a structured and comprehensive review of existing techniques, categorizing them into four core domains: image enhancement, image restoration, image compression and segmentation, and image classification. Representative methods within each domain are critically analyzed in terms of their underlying principles, computational complexity, and applicability across diverse underwater scenarios. Furthermore, the survey highlights emerging trends including deep learning-based approaches, cross-modal information fusion, and resource-efficient designs, offering insights for future development in underwater visual computing and communication systems.
{"title":"Toward Efficient Underwater Visual Perception through Image Enhancement, Compression, and Understanding","authors":"Rongxin Zhu, Lei Sheng, Kaitao Wu, Azzedine Boukerche, Libo Long, Qiuling Yang","doi":"10.1145/3779223","DOIUrl":"https://doi.org/10.1145/3779223","url":null,"abstract":"The growing demand for marine exploration, environmental monitoring, and autonomous underwater operations has elevated the role of underwater image processing in both research and practical applications. However, the acquisition and transmission of underwater visual data are fundamentally constrained by the harsh aquatic environment, where factors such as limited bandwidth, strong light scattering, color distortion, and complex noise severely degrade image quality and restrict data throughput. These challenges not only hinder real-time perception and decision-making but also affect the efficiency of data-driven tasks such as mapping, object recognition, and navigation. To address these issues, a broad spectrum of underwater image processing methods has emerged, aiming to enhance visual clarity, compress data for efficient transmission, restore degraded signals, and enable accurate scene understanding. This survey provides a structured and comprehensive review of existing techniques, categorizing them into four core domains: image enhancement, image restoration, image compression and segmentation, and image classification. Representative methods within each domain are critically analyzed in terms of their underlying principles, computational complexity, and applicability across diverse underwater scenarios. Furthermore, the survey highlights emerging trends including deep learning-based approaches, cross-modal information fusion, and resource-efficient designs, offering insights for future development in underwater visual computing and communication systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"130 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Digital Twins (DT) paradigm has emerged as a powerful tool for simulating and analyzing complex systems in various domains. A DT is a virtual representation of a real-world object(s) whose goal is to accurately emulate real systems, optimize processes, minimize synchronization delays, cut down on overhead, and automate decision-making. DT technology is moving at a faster than expected pace with advances in Artificial Intelligence (AI), Internet of Things (IoT), Distributed Computing, and 5/6G. Being a highly beneficial technology, DT still faces issues of - (1) limited adaptability, (2) incomplete model representation, (3) suboptimal decision making, (4) limited generalization, and (5) scalability and computational efficiency. Reinforcement Learning (RL) offers unsupervised decision-making and intelligence, which can be immensely beneficial in addressing the current challenges faced by DT. This study offers a thorough analysis of the DT paradigm from the standpoint of RL. The survey compares and contrasts existing reinforcement learning-based Digital Twin frameworks, assessing their advantages and disadvantages. Moreover, discussions of approaches highlighting the trade-offs between simulation fidelity and computing complexity is also studied. Additionally, a thorough understanding of the Digital Twins paradigm from a reinforcement learning perspective, is presented as a helpful resource for academics and industry professionals in the field. Finally, future research directions in this developing field at the nexus of digital modeling, simulation, and artificial intelligence is discussed.
{"title":"Digital Twins Paradigm: A Systematic Review from the Reinforcement Learning Perspective","authors":"Shahmir Khan Mohammed, Shakti Singh, Rabeb Mizouni, Hadi Otrok, Ernesto Damiani","doi":"10.1145/3777367","DOIUrl":"https://doi.org/10.1145/3777367","url":null,"abstract":"The Digital Twins (DT) paradigm has emerged as a powerful tool for simulating and analyzing complex systems in various domains. A DT is a virtual representation of a real-world object(s) whose goal is to accurately emulate real systems, optimize processes, minimize synchronization delays, cut down on overhead, and automate decision-making. DT technology is moving at a faster than expected pace with advances in Artificial Intelligence (AI), Internet of Things (IoT), Distributed Computing, and 5/6G. Being a highly beneficial technology, DT still faces issues of - (1) limited adaptability, (2) incomplete model representation, (3) suboptimal decision making, (4) limited generalization, and (5) scalability and computational efficiency. Reinforcement Learning (RL) offers unsupervised decision-making and intelligence, which can be immensely beneficial in addressing the current challenges faced by DT. This study offers a thorough analysis of the DT paradigm from the standpoint of RL. The survey compares and contrasts existing reinforcement learning-based Digital Twin frameworks, assessing their advantages and disadvantages. Moreover, discussions of approaches highlighting the trade-offs between simulation fidelity and computing complexity is also studied. Additionally, a thorough understanding of the Digital Twins paradigm from a reinforcement learning perspective, is presented as a helpful resource for academics and industry professionals in the field. Finally, future research directions in this developing field at the nexus of digital modeling, simulation, and artificial intelligence is discussed.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"93 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inga Miadowicz, Daniel Maldonado Quinto, Michael Felderer
Alongside the vision of autonomous systems, similar system concepts are being discussed in the research fields of highly automated, intelligent, adaptive, autonomic, and organic systems. Although these types of system are studied in scattered research fields that consider them as distinct system classes, they share similar characteristics and are interrelated to some extent. Experts in various fields present a very heterogeneous view on the intersection of autonomous and comparable system concepts, for example, as interchangeable, distinct, or complementary research approaches. Therefore, this study performs a systematic literature review based on more than 300 articles to investigate the intersection of the system classes, emphasizing their similarities, differences, and relationships from the current state of the art.
{"title":"A Systematic Literature Review on the Intersection of Self-X System Classes","authors":"Inga Miadowicz, Daniel Maldonado Quinto, Michael Felderer","doi":"10.1145/3778859","DOIUrl":"https://doi.org/10.1145/3778859","url":null,"abstract":"Alongside the vision of autonomous systems, similar system concepts are being discussed in the research fields of highly automated, intelligent, adaptive, autonomic, and organic systems. Although these types of system are studied in scattered research fields that consider them as distinct system classes, they share similar characteristics and are interrelated to some extent. Experts in various fields present a very heterogeneous view on the intersection of autonomous and comparable system concepts, for example, as interchangeable, distinct, or complementary research approaches. Therefore, this study performs a systematic literature review based on more than 300 articles to investigate the intersection of the system classes, emphasizing their similarities, differences, and relationships from the current state of the art.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ping Wang, Shishir Nagaraja, Aurélien Bourquard, Haichang Gao, Jeff Yan
Acoustic side channels (ASCs) have been discovered for several decades, highlighting the tangible security risks posed by unintended sound emissions from computing and electronic systems. Their existence has drawn considerable attention from researchers, driving rapid progress in both attack methodologies and defense mechanisms across a wide range of scenarios. In this paper, we provide a state-of-the-art analysis of ASCs, covering all the significant academic research in the area. First, we clarify existing ambiguities and conceptual confusion, proposing a clear definition of ASC. Second, we analyse the characteristics of known ASCs, discuss their security implications, and propose the first taxonomy. Next, we summarise attack techniques, discuss countermeasures, and identify areas for future research. We also link side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.
{"title":"SoK: Acoustic Side Channels","authors":"Ping Wang, Shishir Nagaraja, Aurélien Bourquard, Haichang Gao, Jeff Yan","doi":"10.1145/3778350","DOIUrl":"https://doi.org/10.1145/3778350","url":null,"abstract":"Acoustic side channels (ASCs) have been discovered for several decades, highlighting the tangible security risks posed by unintended sound emissions from computing and electronic systems. Their existence has drawn considerable attention from researchers, driving rapid progress in both attack methodologies and defense mechanisms across a wide range of scenarios. In this paper, we provide a state-of-the-art analysis of ASCs, covering all the significant academic research in the area. First, we clarify existing ambiguities and conceptual confusion, proposing a clear definition of ASC. Second, we analyse the characteristics of known ASCs, discuss their security implications, and propose the first taxonomy. Next, we summarise attack techniques, discuss countermeasures, and identify areas for future research. We also link side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AI systems are not only becoming better in solving complex reasoning challenges, but also in performing creative tasks. One of the creative tasks where AI systems still struggle to achieve human performance, however, is humor processing, for which mixed results have been reported. Therefore, the goal of this survey is to categorize recent research in computational humor modeling in order to identify current trends, advancements, and remaining gaps. The scope of this work is broader than previous survey papers, as we tackle not only text-based models, but also multimodal models, and discuss a variety of detection and generation tasks.
{"title":"Computational Humor Modeling: A Survey on the State of the Art","authors":"Jens Lemmens, Victor De Marez","doi":"10.1145/3778357","DOIUrl":"https://doi.org/10.1145/3778357","url":null,"abstract":"AI systems are not only becoming better in solving complex reasoning challenges, but also in performing creative tasks. One of the creative tasks where AI systems still struggle to achieve human performance, however, is humor processing, for which mixed results have been reported. Therefore, the goal of this survey is to categorize recent research in computational humor modeling in order to identify current trends, advancements, and remaining gaps. The scope of this work is broader than previous survey papers, as we tackle not only text-based models, but also multimodal models, and discuss a variety of detection and generation tasks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"89 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenjie Li, Mei Wang, Kai Zhang, Juncheng Li, Xiaoming Li, Yuhang Zhang, Guangwei Gao, Zhanyu Ma
Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to significant progress in FR methods. In this paper, we begin by examining the prevalent factors responsible for real-world LQ images and introduce degradation techniques used to synthesize LQ images. We also discuss notable benchmarks commonly utilized in the field. Next, we categorize FR methods based on different tasks and explain their evolution. Furthermore, we explore the various facial priors commonly utilized in restoration and discuss strategies to enhance their effectiveness. In the experimental section, we thoroughly evaluate the performance of state-of-the-art FR methods across various tasks using a unified benchmark. We analyze their performance from different perspectives. Finally, we discuss real-world applications and challenges faced in the field of FR, propose potential directions for future advancements. The open-source repository corresponding to this work can be found at https://github.com/24wenjie-li/Awesome-Face-Restoration .
{"title":"Survey on Deep Face Restoration: From Non-blind to Blind and Beyond","authors":"Wenjie Li, Mei Wang, Kai Zhang, Juncheng Li, Xiaoming Li, Yuhang Zhang, Guangwei Gao, Zhanyu Ma","doi":"10.1145/3778162","DOIUrl":"https://doi.org/10.1145/3778162","url":null,"abstract":"Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to significant progress in FR methods. In this paper, we begin by examining the prevalent factors responsible for real-world LQ images and introduce degradation techniques used to synthesize LQ images. We also discuss notable benchmarks commonly utilized in the field. Next, we categorize FR methods based on different tasks and explain their evolution. Furthermore, we explore the various facial priors commonly utilized in restoration and discuss strategies to enhance their effectiveness. In the experimental section, we thoroughly evaluate the performance of state-of-the-art FR methods across various tasks using a unified benchmark. We analyze their performance from different perspectives. Finally, we discuss real-world applications and challenges faced in the field of FR, propose potential directions for future advancements. The open-source repository corresponding to this work can be found at <jats:italic toggle=\"yes\">https://github.com/24wenjie-li/Awesome-Face-Restoration</jats:italic> .","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"141 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ikram Darif, Ghizlane El Boussaidi, Segla Kpodjedo, Cristiano Politowski
Requirements are critical artifacts of the software development life-cycle. They express capabilities that the system should provide, guiding both the development and testing process. Given their significance, requirements specification has attracted the interest of researchers and practitioners in recent years. Requirements specification is an activity where requirements are specified, i.e., documented. In this context, Controlled Natural Languages (CNL) were proposed as a compromise between the ambiguity of natural language and the complexity of formal languages. CNLs enable the specification of requirements using accurate statements that can be processed automatically, while remaining understandable by stakeholders. In this paper, we perform a Systematic Literature Review (SLR) to identify, categorize, and compare CNL approaches for requirements specification. The SLR covers 133 primary studies published between 2000 and 2024. We evaluate them according to seven dimensions: context, scope, targeted requirements types, specification technique, tool support, validation method, and adoption. We provide a categorization framework that summarizes the evaluated dimensions, and we identify directions for future research. Our main results reveal: (1) four types of CNL: standalone templates, requirement patterns, elementary templates, and linguistic rules, (2) limited support for automated tools and domain vocabulary usage, and (3) lack of validation through case studies and limited adoption for the majority of approaches.
{"title":"Controlled Natural Language for Requirements Specification: A Systematic Literature Review","authors":"Ikram Darif, Ghizlane El Boussaidi, Segla Kpodjedo, Cristiano Politowski","doi":"10.1145/3778169","DOIUrl":"https://doi.org/10.1145/3778169","url":null,"abstract":"Requirements are critical artifacts of the software development life-cycle. They express capabilities that the system should provide, guiding both the development and testing process. Given their significance, requirements specification has attracted the interest of researchers and practitioners in recent years. Requirements specification is an activity where requirements are specified, i.e., documented. In this context, Controlled Natural Languages (CNL) were proposed as a compromise between the ambiguity of natural language and the complexity of formal languages. CNLs enable the specification of requirements using accurate statements that can be processed automatically, while remaining understandable by stakeholders. In this paper, we perform a Systematic Literature Review (SLR) to identify, categorize, and compare CNL approaches for requirements specification. The SLR covers 133 primary studies published between 2000 and 2024. We evaluate them according to seven dimensions: context, scope, targeted requirements types, specification technique, tool support, validation method, and adoption. We provide a categorization framework that summarizes the evaluated dimensions, and we identify directions for future research. Our main results reveal: (1) four types of CNL: standalone templates, requirement patterns, elementary templates, and linguistic rules, (2) limited support for automated tools and domain vocabulary usage, and (3) lack of validation through case studies and limited adoption for the majority of approaches.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fawad Khan, Syed Aziz Shah, Shahzaib Tahir, Yazeed Yasin Ghadi, Syed Ikram Shah, Adnan Zahid, Jawad Ahmad, Qammer Hussain Abbasi
The efficient and secure processing of confidential health data always remained an important challenge for healthcare professionals and policymakers as this information needs to be shared among several parties for both data analytics and improved health treatments. In this regard, Privacy Enhancing Technologies (PETs) have already shown great potential in deploying intelligent healthcare systems for improved prognosis and diagnosis. This article explains important privacy-preserving techniques by focusing on their security models and performance issues. It specifically discusses libraries and tools that can be used to implement a particular PET model. Moreover, a detailed comparison is provided to highlight the strengths and weaknesses of each of the privacy enhancing approaches. It further sheds light on the security requirements of the health sector and summarizes state-of-the-art homomorphic encryption, secure multi-party computation, differential privacy, and trusted execution environment approaches used in the healthcare setting. Finally, important parameters are discussed that must be kept in consideration while choosing an optimal PET. The survey is concluded by presenting some future directions to improve the performance of PETs and their usage in the healthcare domain. To the best of our knowledge, it is the first paper that comprehensively discusses PETs in the context of healthcare.
{"title":"Privacy Enhancing Technologies for Intelligent Healthcare: Research Challenges and Opportunities","authors":"Fawad Khan, Syed Aziz Shah, Shahzaib Tahir, Yazeed Yasin Ghadi, Syed Ikram Shah, Adnan Zahid, Jawad Ahmad, Qammer Hussain Abbasi","doi":"10.1145/3771543","DOIUrl":"https://doi.org/10.1145/3771543","url":null,"abstract":"The efficient and secure processing of confidential health data always remained an important challenge for healthcare professionals and policymakers as this information needs to be shared among several parties for both data analytics and improved health treatments. In this regard, Privacy Enhancing Technologies (PETs) have already shown great potential in deploying intelligent healthcare systems for improved prognosis and diagnosis. This article explains important privacy-preserving techniques by focusing on their security models and performance issues. It specifically discusses libraries and tools that can be used to implement a particular PET model. Moreover, a detailed comparison is provided to highlight the strengths and weaknesses of each of the privacy enhancing approaches. It further sheds light on the security requirements of the health sector and summarizes state-of-the-art homomorphic encryption, secure multi-party computation, differential privacy, and trusted execution environment approaches used in the healthcare setting. Finally, important parameters are discussed that must be kept in consideration while choosing an optimal PET. The survey is concluded by presenting some future directions to improve the performance of PETs and their usage in the healthcare domain. To the best of our knowledge, it is the first paper that comprehensively discusses PETs in the context of healthcare.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"30 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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