Results 31 to 40 of about 212,227 (315)
Investigation and Application of Differential Privacy in Bitcoin
IEEE Access, 2022 Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy.
Merve Can Kus, Albert Levi
doaj +1 more source
Aggressive Behavior, Volume 49, Issue 1, Page 85-95, January 2023., 2023 Abstract The past two decades have produced extensive evidence on the manifold and severe outcomes for victims of aggression exposure in the workplace. However, due to the dominating individual‐centered approach, most findings miss a social network perspective.
Alexander Herrmann +2 more
wiley +1 more source
Image quality improvement in low‐dose chest CT with deep learning image reconstruction
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening ...
Qian Tian +7 more
wiley +1 more source
Learning With Differential Privacy [PDF]
, 2020 The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized ...
Poushali Sengupta +2 more
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Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Background Ultrasonography (US) and 99mTechnetium‐sestamibi scintigraphy (99mTc‐MIBI) are currently first‐line imaging modalities to localize parathyroid adenomas with sensitivities of 80% and 84%, respectively. Therefore, finding other modalities to further improve the diagnostic accuracy for preoperative localization is critically needed ...
Fangyi Liu +7 more
wiley +1 more source
2022 IEEE International Symposium on Information Theory (ISIT), 2022 To appear in the 2022 IEEE International Symposium on Information ...
Zhou, Ziqi +5 more
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Differential Privacy for Directional Data [PDF]
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021 Directional data is an important class of data where the magnitudes of the data points are negligible. It naturally occurs in many real-world scenarios: For instance, geographic locations (approximately) lie on a sphere, and periodic data such as time of day, or day of week can be interpreted as points on a circle.
Weggenmann, Benjamin +1 more
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Digital radiography image quality evaluation using various phantoms and software
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Purpose To investigate the effect of the exposure parameters on image quality (IQ) metrics of phantom images, obtained automatically using software or from visual evaluation. Methods Three commercial phantoms and a homemade phantom constructed according to the instructions given in the IAEA Human Health Series No.
Ioannis A. Tsalafoutas +4 more
wiley +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the formulation, implementation, and interpretation of privacy guarantees.
Gao, Jie, Gong, Ruobin, Yu, Fang-Yi
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Deep Learning with Differential Privacy [PDF]
Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016 Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets.
Martı́n Abadi +6 more
openalex +4 more sources