Fostering social media user intentions: AI-enabled privacy and intrusiveness concerns by Muhammad Haroon Shoukat, Islam Elgammal, Kareem M. Selem, Ali Elsayed Shehata

“…Findings – Privacy concerns significantly affect perceived usefulness. …”
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Privacy protection method on time-series data publication by Dong YU, Hai-yan KANG

“…A differential privacy model was proposed based on the sampling filtering and the mechanism of evaluation.Firstly,fixed sampling method was used to sample the original data and the non-sampling data be published directly.Secondly,for the sampling date,utilize the differential privacy mechanism to add the noise.Then,use Kalman to correct the sampling date.Finally,use the mutual information to evaluate data under different sampling intervals.Through the experiment,it is proved that the mechanism can achieve a good balance between the practicality and protective.…”
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Privacy guarantees for personal mobility data in humanitarian response by Nitin Kohli, Emily Aiken, Joshua E. Blumenstock

“…Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. …”
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Synthetic data for privacy-preserving clinical risk prediction by Zhaozhi Qian, Thomas Callender, Bogdan Cebere, Sam M. Janes, Neal Navani, Mihaela van der Schaar

“…Compared with other privacy-enhancing approaches—such as federated learning—analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. …”
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Secure data sharing technology of medical privacy data in the Web 3.0 by Shusheng Guo, Cheng Chen, Qing Tong

“…Finally, data users apply to multiple parties for joint secure computing to obtain the use of private data. …”
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Proactive Data Categorization for Privacy in DevPrivOps by Catarina Silva, João P. Barraca, Paulo Salvador

“…PsDC is a data-categorization model designed for integration with the DevPrivOps methodology and for use in privacy-quantification models. …”
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DPLQ: Location‐based service privacy protection scheme based on differential privacy by Qingyun Zhang, Xing Zhang, Mingyue Wang, Xiaohui Li

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Enhancing healthcare data privacy and interoperability with federated learning by Adil Akhmetov, Zohaib Latif, Benjamin Tyler, Adnan Yazici

“…Unlike traditional centralized learning (CL) solutions that require data centralization, our platform uses local model learning, which naturally improves data privacy. …”
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Research on data integration privacy preservation mechanism for DaaS by Zhi-gang ZHOU, Hong-li ZHANG, Xiang-zhan YU, Pan-pan LI

“…In addition, the corres ding fake data set was used to assure the balanced distribution of data in each part, which realized privacy protection of data integration. …”
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Survey of artificial intelligence data security and privacy protection by Kui REN, Quanrun MENG, Shoukun YAN, Zhan QIN

“…Artificial intelligence and deep learning algorithms are developing rapidly.These emerging techniques have been widely used in audio and video recognition, natural language processing and other fields.However, in recent years, researchers have found that there are many security risks in the current mainstream artificial intelligence model, and these problems will limit the development of AI.Therefore, the data security and privacy protection was studied in AI.For data and privacy leakage, the model output based and model update based problem of data leakage were studied.In the model output based problem of data leakage, the principles and research status of model extraction attack, model inversion attack and membership inference attack were discussed.In the model update based problem of data leakage, how attackers steal private data in the process of distributed training was discussed.For data and privacy protection, three kinds of defense methods, namely model structure defense, information confusion defense and query control defense were studied.In summarize, the theoretical foundations, classic algorithms of data inference attack techniques were introduced.A few research efforts on the defense techniques were described in order to provoke further research efforts in this critical area.…”
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A Review of Differential Privacy in Individual Data Release by Jun Wang, Shubo Liu, Yongkai Li

“…The rapid development of mobile technology has improved users' quality of treatment, and tremendous amounts of medical information are readily available and widely used in data analysis and application, which bring on serious threats to users' privacy. …”
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Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
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Differential Privacy and Collective Bargaining over Workplace Data by Sandy J.J. Gould

“…I argue that using differential privacy, a technique for processing data that makes it harder to determine who contributed data to a dataset, would remove an obstacle to employers sharing workplace data with worker representatives.…”
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Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
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Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets by Louis Philippe Sondeck, Maryline Laurent

“…This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. …”
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My privacy at risk – my guard is on: a study of SNS use among young adults by Meenakshi Handa, Ronika Bhalla, Parul Ahuja

“…Purpose – Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. …”
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Automated Redaction of Personally Identifiable Information on Drug Labels Using Optical Character Recognition and Large Language Models for Compliance with Thailand’s Personal Data... by Parinya Thetbanthad, Benjaporn Sathanarugsawait, Prasong Praneetpolgrang

“…The rapid proliferation of artificial intelligence (AI) across various industries presents both opportunities and challenges, particularly concerning personal data privacy. With the enforcement of regulations like Thailand’s Personal Data Protection Act (PDPA), organizations face increasing pressure to protect sensitive information found in diverse data sources, including product and shipping labels. …”
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Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption by William J. Buchanan, Hisham Ali

“…The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. …”
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Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques by Parihar Bhawana, Kiran Ajmeera, Valaboju Sabitha, Rashid Syed Zahidur, Liyakat Kazi Kutubuddin Sayyad, D R Anita Sofia Liz

“…Traditional solutions tend to use standard techniques like basic data wrapping and cryptographic 'rings'; but, due to the design properties required, they end up as lightweight mechanisms, usually not interpretation-at-all capable because of the need for protecting data during processing - leaving these applications hard to use and maintain long-term, or otherwise, limited to cloud computing and federated learning, when individual data types can be worked on within providers like AWS, Azure, etc, etc; or, even, explaining the results with near total indifference to the underlying big data tools, analytics, or neural architectures. …”
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Privacy self-management and the issue of privacy externalities: of thwarted expectations, and harmful exploitation by Simeon de Brouwer

“…This term, related to similar concepts from the literature on privacy such as ‘networked privacy’ or ‘data pollution’, is used here to bring to light the incentives and exploitative dynamics behind a phenomenon which, I demonstrate, benefits both the user and the data controller to the detriment of third-party data subjects. …”
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