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Data Processing and Exchange
Published in Peng Liu, Wang Chao, Computational Advertising, 2020
Quasi-identifiers and K-anonymity are not the products of the Internet privacy problem, but born in the database world. They have inspired us that the privacy problem will become more challenging when there is abundant background information but sparse behavioral data. Such a challenge is unprecedentedly severe during the data transaction involved with the personalized systems represented by the online advertising and recommendation. Challenge posed by sparse behavioral dataIn the personalized Internet applications such as computational advertising, our description of an individual user not only relies on his/her basic information as shown in the above example, but a large amount of his/her behavioral data which are extremely sparse. In other words, it is hardly possible for any two users to have identical behavioral data, and the K-anonymity solution is not applicable every time. Well, can we employ the behavioral data to back induce the user privacy? The answer is yes, and there are real cases in this regard.
Blockchain
Published in Haishi Bai, Zen of Cloud, 2019
Data encrypted by an RSA private key can be decrypted only with the corresponding public key (and vice versa). This characteristic can be used to ensure privacy in communication. For example, when Alice tries to send Bob a message, she uses Bob's public key to encrypt the data and sends the data over the Internet. Because only Bob has the private key, he's the only one who can decrypt the data. So, even if data is passed along the untrusted Internet, privacy is maintained between Alice and Bob, as shown in Figure 9.4.
Patents and Standards
Published in Alfred J. Menezes, Paul C. van Oorschot, Scott A. Vanstone, Handbook of Applied Cryptography, 2018
Alfred J. Menezes, Paul C. van Oorschot, Scott A. Vanstone
Table 15.10 lists selected security-related Internet RFCs. The hashing algorithms MD2, MD4, and MD5 are specified in RFCs 1319-1321, respectively. The Internet Privacy-Enhanced Mail (PEM) specifications are given in RFCs 1421-1424.
Trade off Cybersecurity Concerns for Co-Created Value
Published in Journal of Computer Information Systems, 2020
Tao Hu, Kai-Yu Wang, Wenhai Chih, Xiu-Hua Yang
According to the belief-behavior framework, individuals assess the psychological variables regarding the consequences rising from a behavior, evaluate the desirability of these consequences, and make decisions about behavioral intention. The reasoning line implies a cost-benefit paradigm that steers individuals’ cognitions in behavioral responses.22–26 In the use of social media, the cost factors have been identified including usage efforts (ease of use), information risk, and Internet privacy risk [e.g., 7, 28, 29] Also following the belief-behavior framework and the TAM tradition, extant studies have captured the benefit factors of social media use including personal Internet interest7, perceived usefulness29, and social value.28 Other factors that have been identified related to the social media use include habit30,31, privacy calculus/compromise7,32, and addiction of social media use.33
An Identification of Factors Motivating Individuals’ Use of Cloud-Based Services
Published in Journal of Computer Information Systems, 2018
Gary Garrison, Carl M. Rebman, Sang Hyun Kim
Information privacy has been evaluated as multidimensional construct consisting of four dimensions of concern: collection of personal information, unauthorized secondary use of personal information, errors in the data collected whether by accident or intention, and improper access by unauthorized individuals [66]. Similar to Brown and Muchira’s [13] investigation between Internet privacy concerns and online purchase behavior, this study evaluates two dimensions of information privacy as being primary concerns for users of cloud-based services: collection and improper access.
Explaining Diversity and Conflicts in Privacy Behavior Models
Published in Journal of Computer Information Systems, 2020
Anna Rohunen, Jouni Markkula, Marikka Heikkilä, Markku Oivo
The presented model focuses on the data subjects’ beliefs about privacy and their corresponding behavioral intentions in the Internet transaction domain.5 The model derives from the TRA and its later version, the theory of planned behavior (TPB). It incorporates two primary components of the TRA and the TPB models—beliefs and behavioral intention—to investigate the beliefs that influence the behavioral intention to disclose the personal information needed for Internet transactions. The authors assume that these beliefs can be contradictory by nature and together comprise a set of elements in a data subject’s privacy calculus-type decision process (i.e., their cost-benefit evaluation of their personal information disclosure) that leads to one’s intention to disclose personal information to complete a transaction. Overall, the model is grounded on the expectancy theory; data subjects are assumed to behave in ways that maximize positive outcomes and minimize negative outcomes of their behavior. Following the privacy calculus theory, risk beliefs and confidence and enticement beliefs are incorporated into the model. A direct relation is suggested between two risk beliefs: perceived Internet privacy risks and privacy concerns. In this model, privacy concerns are considered an internalization of the possibility of privacy loss associated with websites in general. Such concerns represent an assessment about what happens to the personal information that the user discloses on the Internet. When defining their model construct for privacy concerns, the authors also refer to the possibility of other parties’ opportunistic behavior related to the submitted personal information. They particularize the concept of privacy concerns by stating that these comprise the data subject’s beliefs about who has access to the disclosed information and how it is used.