物件導向軟體之架構(Object-Oriented Software Construction)探討

This article summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy. We focus on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the trade-offs associated with the privacy and the sharing of personal data. We highlight how the economic analysis of privacy evolved over time, as advancements in information technology raised increasingly nuanced and complex issues. We find and highlight three themes that connect diverse insights from the literature. First, characterizing a single unifying economic theory of privacy is hard, because privacy issues of economic relevance arise in widely diverse contexts. Second, there are theoretical and empirical situations where the protection of privacy can both enhance and detract from individual and societal welfare. Third, in digital economies, consumers' ability to make informed decisions about their privacy is severely hindered because consumers are often in a position of imperfect or asymmetric information regarding when their data is collected, for what purposes, and with what consequences. We conclude the article by highlighting some of the ongoing issues in the privacy debate of interest to economists.

/pdf/the-economics-of-privacy-4luf9jam9p.pdf

The Economics of Privacy

This article summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy. We focus on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the trade-offs associated with the privacy and the sharing of personal data. We highlight how the economic analysis of privacy evolved over time, as advancements in information technology raised increasingly nuanced and complex issues associated with the protection and sharing of personal information. We find and highlight three themes that connect diverse insights from the literature. First, characterizing a single unifying economic theory of privacy is hard, because privacy issues of economic relevance arise in widely diverse contexts. Second, there are theoretical and empirical situations where the protection of privacy can both enhance, and detract from, individual and societal welfare. Third, in digital economies, consumers' ability to make informed decisions about their privacy is severely hindered, because consumers are often in a position of imperfect or asymmetric information regarding when their data is collected, for what purposes, and with what consequences. We conclude the article by highlighting some of the ongoing issues in the privacy debate of interest to economists.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2744818_code1596912.pdf?abstractid=2580411&mirid=1&type=2

We present the largest and most detailed measurement of online tracking conducted to date, based on a crawl of the top 1 million websites. We make 15 types of measurements on each site, including stateful (cookie-based) and stateless (fingerprinting-based) tracking, the effect of browser privacy tools, and the exchange of tracking data between different sites ("cookie syncing"). Our findings include multiple sophisticated fingerprinting techniques never before measured in the wild. This measurement is made possible by our open-source web privacy measurement tool, OpenWPM, which uses an automated version of a full-fledged consumer browser. It supports parallelism for speed and scale, automatic recovery from failures of the underlying browser, and comprehensive browser instrumentation. We demonstrate our platform's strength in enabling researchers to rapidly detect, quantify, and characterize emerging online tracking behaviors.

https://dl.acm.org/doi/pdf/10.1145/2976749.2978313

Online Tracking: A 1-million-site Measurement and Analysis

The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability In this article, we provide the first comprehensive review of these much-discussed tracing app attributes We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large

pdf/a-survey-of-covid-19-contact-tracing-apps-3teotpu4dw.pdf

A Survey of COVID-19 Contact Tracing Apps

Consent Management Providers (CMPs) provide consent pop-ups that are embedded in ever more websites over time to enable streamlined compliance with the legal requirements for consent mandated by the ePrivacy Directive and the General Data Protection Regulation (GDPR). They implement the standard for consent collection from the Transparency and Consent Framework (TCF) (current version v2.0) proposed by the European branch of the Interactive Advertising Bureau (IAB Europe). Although the IAB's TCF specifications characterize CMPs as data processors, CMPs factual activities often qualifies them as data controllers instead. Discerning their clear role is crucial since compliance obligations and CMPs liability depend on their accurate characterization. We perform empirical experiments with two major CMP providers in the EU: Quantcast and OneTrust and paired with a legal analysis. We conclude that CMPs process personal data, and we identify multiple scenarios wherein CMPs are controllers.

Consent Management Platforms under the GDPR: processors and/or controllers?

Runtime enforcement is a common mechanism for ensuring that program executions adhere to constraints specified by a security policy. It is based on two simple ideas: the enforcement mechanism should leave good executions without changes and make sure that the bad ones got amended. From the theory side, a number of papers [6,10,12] provide the precise characterization of good executions that can be captured by a security policy and thus enforced by a specific mechanism. Unfortunately, those theories do not distinguish what happens when an execution is actually bad (the practical case). The theory only says that the outcome of enforcement mechanism should be "good" but not how far should the bad execution be changed.

If we consider a real-life example of a drug dispensation process in a hospital the notion of security automata or even edit automata would stop all requests by all doctors on all drugs and all dispensation protocols, as soon as a doctor forgot to insert the research protocol number.

In this paper we explore a set of policies called iterative properties that revises the notion of good traces in terms of repeated iterations. We start discussing how an enforcement mechanism can actually deal with bad executions (and not just only the good ones).

/pdf/towards-practical-enforcement-theories-177lkudarb.pdf

Towards Practical Enforcement Theories

Although they address the same problem, the so-called “centralized” and “decentralized” approaches to COVID-19 proximity tracing rely on different threat model assumptions. The goal of this document is to analyze the impact of these two options in terms of privacy, security and reliability.

The main objective of the decentralized approach is to protect users against a malicious server or a state-level adversary and to prevent the leak of sensitive data due to attacks or negligence at the server side. Therefore, the role of the server is reduced as much as possible, and the exposure verification is performed on the user device. In contrast, the centralized approach puts more emphasis on the protection of users against other malicious users trying to infer who is infected. Hence, the role of the server in the centralized approach is more important, including the verification of exposure.

This design choice involves different privacy risks:
● The decentralized approach provides many opportunities to malicious or curious users (through wide scale and undetectable attacks or during normal usage) to infer the identity of infected users or to monitor specific areas. These privacy risks coming from users (e.g., neighbors) can easily lead to abuses as well as stigmatization and harassment of diagnosed users. On the positive side, the server learns little information about users.
● In the centralized approach, in contrast, the capability of users to learn who is infected is drastically limited. This better protection however comes at the cost of relying on a server which is able to learn some information about users.

Law enforcement agencies and third parties colluding with the server are sources of risk in both approaches but they do not concern the same population. Only infected users who consent to declare themselves are concerned in the decentralized approach, while these risks concern all users (infected or not) in the centralized approach. However, the likelihood of these risks needs to be assessed and balanced, as all other risks, with the potential benefits of these applications in the fight against COVID-19. To this respect, the centralized approach can bring added value because the health authority is aware of the number of exposed people and can use it both for statistical purposes and to easily adjust the risk calculation algorithm (to decide if a user should be classified as “at risk”).

https://hal.inria.fr/hal-02570676/document

Proximity Tracing Approaches - Comparative Impact Analysis

We propose a rigorous comparison of two widely known dynamic information flow mechanisms: Secure Multi-Execution (SME) and Multiple Facets (MF). Informally, it is believed that MF simulates SME while providing better performance. Formally, it is well known that SME has stronger soundness guarantees than MF.

/pdf/spot-the-difference-secure-multi-execution-and-multiple-aaj2ebg819.pdf

Spot the Difference: Secure Multi-execution and Multiple Facets

Runtime enforcement is a common mechanism for ensuring that program executions adhere to constraints specified by a security policy. It is based on two simple ideas: the enforcement mechanism should leave good executions without changes transparency and make sure that the bad ones got amended soundness. From the theory side, a number of papers Hamlen et al., Ligatti et al., Talhi et al. provide the precise characterization of good executions that can be captured by a security policy and thus enforced by mechanisms like security automata or edit automata.Unfortunately, transparency and soundness do not distinguish what happens when an execution is actually bad the practical case. They only tell that the outcome of enforcement mechanism should be “good” but not how far the bad execution should be changed. So we cannot formally distinguish between an enforcement mechanism that makes a small change and one that drops the whole execution.In this paper we explore a set of policies called iterative properties that revises the notion of good executions in terms of repeated iterations. We propose an enforcement mechanism that can deal with bad executions and not only the good ones in a more predictable way by eliminating bad iterations.

/pdf/iterative-enforcement-by-suppression-towards-practical-2b5h845jx0.pdf

Iterative enforcement by suppression: Towards practical enforcement theories

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