During pandemics, health officials usually recommend access monitoring and regulation protocols/systems in places that are major activity centres. As organizations adhere to those recommendations, they often fail to implement proper privacy requirements to prevent privacy loss of the users of those protocols or systems. This is a very timely issue as health authorities across the world are increasingly putting these regulations in place to mitigate the spread of the current pandemic. A number of solutions have been proposed to mitigate these privacy issues existing in current models of contact tracing or access regulations systems. However, a prevalent pattern among these solutions are they mainly focus on protecting users privacy from server side and involve Bluetooth based ephemeral identifier exchange between users. Another pattern is all the current solutions try to solve the problem in city-wide or nation-wide level. In this paper, we propose a system, PAARS, which approaches the privacy issues in access monitoring/regulation systems from a micro level. We solve the privacy issues in access monitoring/regulation systems without any exchange of any ephemeral identifiers between users. Moreover, our proposed system provides privacy on both server side and the user side by using secure hashing and differential privacy mechanism.
As electronic data becomes the lifeline of modern society, privacy concerns increase. These concerns are reflected by the European Union's enactment of the General Data Protection Regulation (GDPR), one of the most comprehensive and robust privacy regulations globally. This project aims to evaluate and highlight associations between eHealth systems' policies and personal data privacy regulations. Using bias-corrected Cramer's V and Thiel's U tests, we found weak and zero associations between e-health systems' rules and protections for data privacy. A simple decision tree model is trained, whic…
Internet of Things (IoT) applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data protection laws. However, doing so can be a difficult and challenging task. Recent research has revealed that developers typically face difficulties when complying with regulations. One key reason is that, at times, regulations are vague, and could be challenging to extract and enact such legal requirements. In our research paper, we have conducted a systemat…
The lack of user experience standards in regulations for data privacy and data portability in the health sector increases the cost of leaving a network provider while not protecting the patient's privacy, directly impacting people's health. Furthermore, user in-app options for data sharing and portability in the health sector's applications make it difficult to transfer data between providers while facilitating privacy breaches. Moreover, it leaves users unaware of occasional past unauthorized data access episodes. In this article, we propose an extension for the traditional design heuristics …
Privacy protection raises great attention on both legal levels and user awareness. To protect user privacy, countries enact laws and regulations requiring software privacy policies to regulate their behavior. However, privacy policies are written in natural languages with many legal terms and software jargon that prevent users from understanding and even reading them. It is desirable to use NLP techniques to analyze privacy policies for helping users understand them. Furthermore, existing datasets ignore law requirements and are limited to English. In this paper, we construct the first Chinese…
Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union's General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capable of performing privacy-preserving computations on the …
A privacy policy is a document that states how a company intends to handle and manage their customers' personal data. One of the problems that arises with these privacy policies is that their content might violate data privacy regulations. Because of the enormous number of privacy policies that exist, the only realistic way to check for legal inconsistencies in all of them is through an automated method. In this work, we use Natural Language Inference (NLI) techniques to compare privacy regulations against sections of privacy policies from a selection of large companies. Our NLI model uses pre…
Governments, Healthcare, and Private Organizations in the global scale have been using digital tracking to keep COVID-19 outbreaks under control. Although this method could limit pandemic contagion, it raises significant concerns about user privacy. Known as ~"Contact Tracing Apps", these mobile applications are facilitated by Cellphone Service Providers (CSPs), who enable the spatial and temporal real-time user tracking. Accordingly, it might be speculated that CSPs collect information violating the privacy policies such as GDPR, CCPA, and others. To further clarify, we conducted an in-depth …
University of Canterbury（坎特伯雷大学）；University of Bonn（波恩大学）；University of Auckland（奥克兰大学）
This paper focuses on some shortcomings in current privacy and data protection regulations' ability to adequately address the ramifications of AI-driven data processing practices, in particular where data sets are combined and processed by AI systems. We raise attention to two regulatory anomalies related to two fundamental assumptions underlying traditional privacy and data protection approaches: (1) Only Personally Identifiable Information (PII) and Personal Data (PD) require privacy protection: Privacy and data protection regulations are only triggered with respect to PII/PD, but not anonym…
The new General Data Protection Regulation (GDPR) will take effect in May 2018, and hence, designing compliant data protection policies and system architectures became crucial for organizations to avoid penalties. Unfortunately, the regulations given in a textual format can be easily misinterpreted by the policy and system designers, which also making the conformance check error-prone for auditors. In this paper, we apply formal approach to facilitate systematic design of policies and architectures in an unambiguous way, and provide a framework for mathematically sound conformance checks again…
Digital and physical footprints are a trail of user activities collected over the use of software applications and systems. As software becomes ubiquitous, protecting user privacy has become challenging. With the increase of user privacy awareness and advent of privacy regulations and policies, there is an emerging need to implement software systems that enhance the protection of personal data processing. However, existing data protection and privacy regulations provide key principles in high-level, making it difficult for software engineers to design and implement privacy-aware systems. In th…
The GDPR, or the Datenschutz Grundverordnung (DSGVO) in German, is an EU Law which addresses the subject of safeguarding privacy of personal data of the citizens of the EU and EEA. It also specifies how data the collected data might be transported out of the EU/EEA. It is the first genuine effort to unify the plethora of disparate privacy regulations put forward by different regulatory bodies. The GDPR aims to not only give more control over their personal data to the citizens, but also make conformance for businesses easier by defining unified guidelines. It also presses businesses, especiall…
Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyze different data protection and privacy-preserving techniques in the context of big data an…
Some organizations use software applications to manage their customers' personal, medical, or financial information. In the United States, those software applications are obligated to preserve users' privacy and to comply with the United States federal privacy laws and regulations. To formally guarantee compliance with those regulations, it is essential to extract and model the privacy rules from the text of the law using a formal framework. In this work we propose a goal-oriented framework for modeling and extracting the privacy requirements from regulatory text using natural language process…
Machine Learning on Big Data gets more and more attention in various fields. Even so privacy-preserving techniques become more important, even necessary due to legal regulations such as the General Data Protection Regulation (GDPR). On the other hand data is often distributed among various parties. Especially in the medical context there are several data holders, e.g. hospitals and we need to deal with highly sensitive values. A real world scenario would be data that is held in an electronic patient record that is available in many countries by now. The medical data is encrypted. Users (e.g. p…
Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the "gig" economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms…
Despite the increasing awareness from academia, civil society and media to the issue of child manipulation online, the current EU regulatory system fails at providing sufficient levels of protection. Given the universality of the issue, there is a need to combine and further these scattered efforts into a unitary, multidisciplinary theory of digital manipulation that identifies causes and effects, systematizes the technical and legal knowledge on manipulative and addictive tactics, and to find effective regulatory mechanisms to fill the legislative gaps. In this paper we discuss manipulative a…
The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure. In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV. However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, b…
The current business model for existing recommender services is centered around the availability of users' personal data at their side whereas consumers have to trust that the recommender service providers will not use their data in a malicious way. With the increasing number of cases for privacy breaches, different countries and corporations have issued privacy laws and regulations to define the best practices for the protection of personal information. The data protection directive 95/46/EC and the privacy principles established by the Organization for Economic Cooperation and Development (O…
Cyber Technology Institute, De Montfort University（德蒙福特大学网络技术研究所）
It is well-known that most users do not read privacy policies, but almost all users tick the box to agree with them. In this paper, we analyze the 25-year history of privacy policies using methods from transparency research, machine learning, and natural language processing. Specifically, we collect a large-scale longitudinal corpus of privacy policies from 1996 to 2021 and analyze the length and readability of privacy policies as well as their content in terms of the data practices they describe, the rights they grant to users, and the rights they reserve for their organizations. We pay parti…
In order for VASPs to fulfill the regulatory requirements from the FATF and the Travel Rule, VASPs need access to truthful information regarding originators, beneficiaries and other VASPs involved in a virtual asset transfer instance. Additionally, in seeking data regarding subjects (individuals or organizations) VASPs are faced with privacy regulations such as the GDPR and CCPA. In this paper we a propose privacy-preserving claims issuance model that carries indicators of the provenance of the data and the algorithms used to derive the claim or assertion. This allows VASPs to obtain originato…
L3S Research Center, Leibniz University（莱布尼茨大学L3S研究中心）；University of Applied Sciences Osnabrück（奥斯纳布吕克应用科学大学）；University of Applied Sciences and Arts Hannover（汉诺威应用科学与艺术大学）
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector. However, health data is highly sensitive and subject to regulations such as the General Data Protection Regulation (GDPR), which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, though the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this paper, we review the existing anonymization t…
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
University of Notre Dame（圣母大学）；University of Pennsylvania（宾夕法尼亚大学）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）；George Mason University（乔治梅森大学）
JD Intelligent Cities Research, JD Digits（京东智能城市研究院，京东数科）；JD Intelligent Cities Business Unit, JD Digits（京东智能城市业务部，京东数科）；Institute of Artificial Intelligence, Southwest Jiaotong University（西南交通大学人工智能研究院）
It is commonly observed that the data are scattered everywhere and difficult to be centralized. The data privacy and security also become a sensitive topic. The laws and regulations such as the European Union's General Data Protection Regulation (GDPR) are designed to protect the public's data privacy. However, machine learning requires a large amount of data for better performance, and the current circumstances put deploying real-life AI applications in an extremely difficult situation. To tackle these challenges, in this paper we propose a novel privacy-preserving federated machine learning …
Ample research has demonstrated that compliance with data protection principles remains limited on the web and mobile. For example, almost none of the apps on the Google Play Store fulfil the minimum requirements regarding consent under EU and UK law, while most of them share tracking data with companies like Google/Alphabet and Facebook/Meta and would likely need to seek consent from their users. Indeed, recent privacy efforts and enforcement by Apple have had - in some regards - a more pronounced effect on apps' data practices than the EU's ambitious General Data Protection Regulation (GDPR)…
University of Wisconsin（威斯康星大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
The EU General Data Protection Regulation (GDPR) is one of the most demanding and comprehensive privacy regulations of all time. A year after it went into effect, we study its impact on the landscape of privacy policies online. We conduct the first longitudinal, in-depth, and at-scale assessment of privacy policies before and after the GDPR. We gauge the complete consumption cycle of these policies, from the first user impressions until the compliance assessment. We create a diverse corpus of two sets of 6,278 unique English-language privacy policies from inside and outside the EU, covering th…
Privacy is a key challenge for continued digitalization of health. The forthcoming European General Data Protection Regulation (GDPR) is transforming this challenge into regulatory directives. User consent provisioning and coordinating across data services will be the keys in addressing this challenge. We suggest a privacy-driven architecture that provides tools for providing user consent as a service. This enables managing and reusing private health information between a large amount of data sources, individuals and services, even when they are not known beforehand. The proposed architecture …
University of Marburg（马尔堡大学）；Technical University of Darmstadt（达姆施塔特工业大学）；Georgia Institute of Technology（佐治亚理工学院）；Manipal Institute of Technology（马尼帕尔理工学院）
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especi…
End user privacy is a critical concern for all organizations that collect, process and store user data as a part of their business. Privacy concerned users, regulatory bodies and privacy experts continuously demand organizations provide users with privacy protection. Current research lacks an understanding of organizational characteristics that affect an organization's motivation towards user privacy. This has resulted in a "one solution fits all" approach, which is incapable of providing sustainable solutions for organizational issues related to user privacy. In this work, we have empirically…
Software applications continue to challenge user privacy when users interact with them. Privacy practices (e.g. Data Minimisation (DM), Privacy by Design (PbD) or General Data Protection Regulation (GDPR)) and related "privacy engineering" methodologies exist and provide clear instructions for developers to implement privacy into software systems they develop that preserve user privacy. However, those practices and methodologies are not yet a common practice in the software development community. There has been no previous research focused on developing "educational" interventions such as seri…
IBM Research - Haifa（IBM研究中心海法分中心）；Haifa University Campus（海法大学校园）
There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA), set out strict restrictions and obligations on the collection and processing of personal data. Moreover, machine learning models themselves can be used to derive personal information, as demonstrated by recent membership and attribute inference attacks. Anonymized data, however, is exempt from the obligations set out in these regulations. It is th…
Privacy is an individual choice to determine which personal details can be collected, used and shared. Individual consent and transparency are the core tenets for earning customers trust and this motivates the organizations to adopt privacy enhancing practices while creating the systems. The goal of a privacy-aware design is to protect information in a way that does not increase an adversary's existing knowledge about an individual beyond what is permissible. This becomes critical when these data elements can be linked with the wealth of auxiliary information available outside the system to id…
