Website owners make conscious and unconscious decisions that affect their users, potentially exposing them to privacy and security risks in the process. In this paper we introduce PrivacyScore, an automated website scanning portal that allows anyone to benchmark security and privacy features of multiple websites. In contrast to existing projects, the checks implemented in PrivacyScore cover a wider range of potential privacy and security issues. Furthermore, users can control the ranking and analysis methodology. Therefore, PrivacyScore can also be used by data protection authorities to perform regularly scheduled compliance checks. In the long term we hope that the transparency resulting from the published assessments creates an incentive for website owners to improve their sites. The public availability of a first version of PrivacyScore was announced at the ENISA Annual Privacy Forum in June 2017.
Furthermore, there are numerous scientific studies that have analyzed security (e. ,g., @cite_13 @cite_5 ) and privacy aspects (e. ,g., @cite_24 ) of popular websites. These studies provide insights about the overall state of privacy and the adoption of security technologies on the web. However, as they typically focus on aggregate statistics, they do not create strong incentives for individual site owners to improve. Moreover, they rarely provide sector-specific insights ( @cite_24 is one of the few exceptions). And finally, as the published data is typically not updated after the publication, the results become outdated rather quickly. This is also true for the 2016 municipality survey @cite_27 of , that inspired us to create PrivacyScore.
@cite_24 @cite_5 @cite_27 @cite_13
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The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers. Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. This problem is further complicated by the fact that, in addition to the published data, the adversary may also have access to other resources (e.g., public records and social networks relating individuals), which we call external knowledge. A robust privacy criterion should take this external knowledge into consideration. In this paper, we first describe a general framework for reasoning about privacy in the presence of external knowledge. Within this framework, we propose a novel multidimensional approach to quantifying an adversary's external knowledge. This approach allows the publishing organization to investigate privacy threats and enforce privacy requirements in the presence of various types and amounts of external knowledge. Our main technical contributions include a multidimensional privacy criterion that is more intuitive and flexible than previous approaches to modeling background knowledge. In addition, we provide algorithms for measuring disclosure and sanitizing data that improve computational efficiency several orders of magnitude over the best known techniques. This study investigates the composition of consumer’s security and privacy perceptions of mobile commerce (m-commerce) and the factors shaping these security and privacy perceptions. Based on literature review, we examined the effect of eight determinants: information type, information collection, secondary use of information, error, unauthorized access, location awareness, information transfer, and personalization; on security and privacy concerns in the m-commerce context. Analysis of data from 141 respondents revealed three dimensions for the security and privacy perception construct. Hence, three models were tested to address the impacts of these factors on the three dimensions: consumers’ confidence of information control, concerns on third party, and the awareness of information protection in the mcommerce context. The study has implications for professionals to meet the consumers’ requirements and expectations on security and privacy for m-commerce. With billions of handsets in use worldwide, the quantity of mobility data is gigantic. When aggregated they can help understand complex processes, such as the spread viruses, and built better transportation systems, prevent traffic congestion. While the benefits provided by these datasets are indisputable, they unfortunately pose a considerable threat to location privacy. In this paper, we present a new anonymization scheme to release the spatio-temporal density of Paris, in France, i.e., the number of individuals in 989 different areas of the city released every hour over a whole week. The density is computed from a call-data-record (CDR) dataset, provided by the French Telecom operator Orange, containing the CDR of roughly 2 million users over one week. Our scheme is differential private, and hence, provides provable privacy guarantee to each individual in the dataset. Our main goal with this case study is to show that, even with large dimensional sensitive data, differential privacy can provide practical utility with meaningful privacy guarantee, if the anonymization scheme is carefully designed. This work is part of the national project XData (http: xdata.fr) that aims at combining large (anonymized) datasets provided by different service providers (telecom, electricity, water management, postal service, etc.).