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Security and Privacy Aspects of AI, IoT, Big Data and Blockchain in Healthcare Industry
Published in Pushpa Singh, Divya Mishra, Kirti Seth, Transformation in Healthcare with Emerging Technologies, 2022
Apoorva Joshi, Ambrish Kumar Sharma, Sanjeev Gour, Pratima Guatam
Data Masking basically replaces sensitive data with an unidentifiable value, but it does not use cryptography because the original value cannot be recovered if it is masked. This reason generates a technique of de-identification or masking of PI-personal identifiers, such as pin code, zip code, name, and other methods like suppressing, bucketization, and generalizing quasi-anonymization. Sweeney (2002) and Can et al. (2018) introduced k-anonymity; this method protects against identity disclosure, but it fails when we talk about protect against attribute disclosure. Truta et al. (2006) proposed p-sensitive anonymity, a means of preventing both identity and attribute disclosure. The main difficulty in anonymizing high-dimensional data sets is a common problem for many approaches (Chawala et al., 2005). When we talk about minimizing cost for securing a big data implementation, this technique has a major advantage. When protected data is migrated onto the platform from a secure source, masking eliminates the need for additional security measures to be applied to the data while it is available on platform.
Instructions
Published in Syed R. Rizvi, Microcontroller Programming, 2016
Before we move on to logic instructions that perform inversions, we would like to discuss the concept of data masking. In general, data masking is the process of obscuring (masking) specific data elements within data stores. In our HC11 programming environment, the bits in an operand that need processing will be identified by a mask. The data mask basically blocks parts of the operand to be processed. This may be required for protecting the data, or for filtering the data. We call a mask an 8-bit word that designates the locations of the bits within the operand to be processed. The bits that will be processed will be indicated as 1 in the mask. The bits to be ignored will be indicated as 0 in the mask. As seen in Table 5.13, when the logical AND operation is performed on AccB (1010 10102) and the operand (1101 00102), the result was 1000 00102. The AccB can be called a mask such that the bits with 1 (i.e., b7, b5, b3, b1) are set to 1. This means that these bits of the operand are to be processes. The bits b6, b4, b2, b0 of AccB are set to zero. This means that these bits in the operand are unwanted and should be eliminated. We observe that bits b7, b5, b3, b1 of the operand remain the same in the result 1000 00102. On the other hand, the bits b6, b4, b2, b0 of the operand become zero in the result 1000 00102.
Spatial analysis and modelling
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
How can you ensure location privacy of individuals? This can involve obscuring techniques such as deleting, randomising, anonymising, substituting, shuffling or encrypting, for example. Data masking or data obfuscation techniques of this type relate also to the protection of personal identifiable data, personal sensitive data or commercially-sensitive data.
Deceptive Infusion of Data: A Novel Data Masking Paradigm for High-Valued Systems
Published in Nuclear Science and Engineering, 2022
Arvind Sundaram, Hany Abdel-Khalik, Ahmad Al Rashdan
This section describes a few types of existing data masking methods. While the methods outlined in this section are applicable to data warehouses, the privacy needs of industrial data are significantly different as they are bound by physical laws and other domain-related constraints. Data masking, broadly classified into static and dynamic methods, protects sensitive information from unintended exposure by masking the environment and only providing the necessary information for the target application.5 In static methods, sensitive information is permanently altered via substitution or removing connections between data fields (shuffling). For example, if an application requires records of the gender distribution in a company, the real names of the employees may be substituted by common names to protect their identities. Additionally, the link between their birthday and SSN may be removed by shuffling the fields prior to transmission. Since these changes are permanent, oftentimes a copy of the original database needs to be created. Dynamic methods, on the other hand, mask information as they reach the recipient. Masking out is one such example of dynamic masking where all but the last four digits of bank account numbers are obscured when requested. Dynamic methods do not require the creation of a copy database and can work in real time unlike static procedures. However, they are not suited for environments where data could be written back and corrupted or where the masking procedure is bypassed and the original data are available.22
Sharing secured data on peer-to-peer applications using attribute-based encryption
Published in Journal of Information and Telecommunication, 2021
Nhan Tam Dang, Ha Manh Tran, Sinh Van Nguyen, Marcin Maleszka, Hai-Duong Le
Data encryption mathematically transforms plaintext into ciphertext using a sophisticated algorithm and key. Data encryption can be used for structured data as well as unstructured data. Tokenization randomly generates an alphanumeric value for plaintext and stores the mapping in a database. When a user needs to access the original data, the system looks up the token value and retrieves it. Tokenization is used for structured data and is often seen in payment cards or social security number applications (Díaz-Santiago et al., 2016; Iwasokun et al., 2018; Nxumalo et al., 2014; Stapleton & Poore, 2011). Hashing generates a unique signature of fixed length for a data set. Each specific data set has a unique hash; making minor changes results in an entirely different hash value than the original one, making the information easily spotted if tampering occurs. Hashing cannot be deciphered or reversed back to its plaintext form and is mainly used as information verification. Data masking is a process of replacing confidential data by using fictitious characters. The primary purpose is to protect sensitive, private information when sharing data with third parties. A simple use-case is to replace actual data with null or constant values like ‘XXX-XXX-XXX’ in the social security number field.