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Privacy Breaches through Cyber Vulnerabilities
Published in Amit Kumar Tyagi, Ajith Abraham, A. Kaklauskas, N. Sreenath, Gillala Rekha, Shaveta Malik, Security and Privacy-Preserving Techniques in Wireless Robotics, 2022
S.U. Aswathy, Amit Kumar Tyagi
One group of methods to secure privacy through attention to data is “differential privacy.” Differential privacy is a type of mathematical perturbation introduced into a data set to ensure that a specific individual’s inclusion in that data cannot be detected when summary statistics generated from either a true or differentiated data set are compared to one another. Some of the other methods for increasing the privacy protections for data prior to use in ML models include: homomorphic encryption, secure multi-party computation, and federated learning. Homomorphic encryption preserves data privacy through analysis of encrypted data. Secure multi-party computation is a protocol for collaboration between parties holding information they prefer to keep private from one another without intervention of a trusted third-party actor. Federated learning allows data to be stored and analyzed locally through models or segments of models sent to the user’s device.
Privacy-Preserving Techniques for the 5G-Enabled Location-Based Services
Published in Yulei Wu, Haojun Huang, Cheng-Xiang Wang, Yi Pan, 5G-Enabled Internet of Things, 2019
Chen Wang, Ping Zhao, Haojun Huang, Rui Zhang, Weixing Zhu
In [50], the authors state that they believe that the abovementioned methods have failed to obtain a good trade-off between the desired level of privacy and the usefulness of the LBSs and therefore introduce the notion of differential privacy from statistic databases into LBSs. Differential privacy works on the principle that distort the sensitive data by adding noise to achieve the privacy preserving while keeping the statistic attributes of data. It is independent on the background knowledge that attackers have and provides a rigorous, quantitative representation and proof for privacy exposures. Presently, differential privacy is the most rigorous technique for LBS privacy preserving. However, it does not suit applications in which only a single user is involved.
Security and Privacy in Big Data Cyber-Physical Systems
Published in Yassine Maleh, Mohammad Shojafar, Ashraf Darwish, Abdelkrim Haqiq, Cybersecurity and Privacy in Cyber-Physical Systems, 2019
L. Josephine Usha, J. Jesu Vedha Nayahi
Differential privacy (Gosain and Chugh 2014; Microsoft 2015) is a perturbation-based concept used for ensuring the privacy of information in the cyber-physical system. Differential privacy is a technique that allows the user to get useful information from a large volume of data without violating the privacy of the individual. This is achieved by doing some distortion to the results provided by the database. The amount of distortion is either increased or decreased, based on privacy risk. The higher level of distortion leads to a high level of protection.
Trajectory privacy data publishing scheme based on local optimisation and R-tree
Published in Connection Science, 2023
Peiqian Liu, Duoduo Wu, Zihao Shen, Hui Wang
In Addition, a series of specific models have emerged from the contemporary social background. In 2010, Mohammed et al. (2010) proposed an anonymisation algorithm to implement the LKC-privacy model, which was initially applied to RFID data. The algorithm identifies the minimum violating series of all trajectory data and forms a set of violating sequences, which are the trajectory sequences that do not satisfy the LKC-privacy model. Then the set of violating sequences is globally suppressed to minimise the generation of larger violating sequences. In 2006, Dwork (2008) proposed the differential privacy protection model which aimed to redefine privacy regarding the issue of database leakage. The model assumes that even with background knowledge of some data records in the database, an attacker cannot deduce the existence of a particular data record through analysis such as querying or statistics on the database information. The technique of differential privacy also has strict and standardised mathematical theoretical proofs and evaluation criteria. In follow-up studies, the LKC-privacy model and differential privacy technique are also gradually applied to trajectory data research.
A novel Map-Scan-Reduce based density peaks clustering and privacy protection approach for large datasets
Published in International Journal of Computers and Applications, 2021
When it is known by attackers, then they can easily organize the whole original data. To solve that issue, we propose differential privacy for and , respectively. This differential privacy approach guaranteed privacy and protect and . Differential privacy is an attack restricted model which ensures privacy and adversaries cannot infer an individual’s presence in a dataset from the random output, which has all remaining individuals. It is used in cyber security that protects personal data. It is possible to collect, share, and group information about any object or sample, while maintaining the privacy for single users. In several aspects, DP is a special and revolutionary algorithm. The reasons are as follows: Underlying data do not require modified or distorted in any aspect. It provides high quality, without leaking privacy.A new concept ‘Distortion’ is concentrated here to answer ‘Posteriori.’ It keeps the aggregate cost for privacy.
Preventing Reverse Engineering of Critical Industrial Data with DIOD
Published in Nuclear Technology, 2023
Arvind Sundaram, Hany S. Abdel-Khalik, Mohammad G. Abdo
Commonly used sanitization methods include data masking techniques such as substitution and shuffling that conceal the proprietary information while attempting to provide adequate information for BI purposes.19,20 If the statistical inference is to be performed on the sensitive information itself, differential privacy21 and encryption22 are commonly used to prevent reverse engineering and unauthorized access to the data, respectively. However, encryption often has massive overhead costs and may not be feasible for large quantities of data found in data warehouses.23 Differential privacy, on the other hand, involves the injection of noise (typically Laplacian) to “fuzzy” the data and may be counterproductive for AI/ML applications that are often sensitive to noise and noiselike perturbations.24 For example, in the 2020 census data that employed differential privacy to protect users, it is acknowledged that rural areas will typically see a greater variance from the raw data than urban areas with regard to population count, and inference on smaller subpopulations is affected more than larger ones.25 On a fundamental level, differential privacy assumes a trade-off between the utility and the privacy of the data; i.e., providing perfect information leads to a loss in privacy whereas having perfect privacy renders the data unusable/indistinguishable due to the addition of noise.26 With regard to sensor data, however, data masking techniques must preserve fundamental correlations among the data that are derived from physical laws. Using the analogy of the census data, differential privacy may lead to populations less than one, unpopulated areas with an assigned population, households with children but no adults, etc.