In this paper, we develop a user-centric privacy framework for quantitatively assessing the exposure of personal information in open settings. Our formalization addresses key-challenges posed by such open settings, such as the unstructured dissemination of heterogeneous information and the necessity of user- and context-dependent privacy requirements. We propose a new definition of information sensitivity derived from our formalization of privacy requirements, and, as a sanity check, show that hard non-disclosure guarantees are impossible to achieve in open settings.
After that, we provide an instantiation of our framework to address the identity disclosure problem, leading to the novel notion of d-convergence. d-convergence is based on indistinguishability of entities and it bounds the likelihood with which an adversary successfully links two profiles of the same user across online communities.
Finally, we provide a large-scale evaluation of our framework on a collection of 15 million comments collected from the Online Social Network Reddit. Our evaluation validates the notion of d-convergence for assessing the linkability of entities in our data set and provides deeper insights into the data set's structure.
Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where high- speed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their pr…
As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importance. Today, privacy controls are enforced by data curators with full access to data in the clear. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. This motivates the need for a new approach to data privacy that can provide strong assurance and c…
Aiming at the privacy preservation of dynamic Web service composition, this paper proposes a SDN-based runtime security enforcement approach for privacy preservation of dynamic Web service composition. The main idea of this approach is that the owner of service composition leverages the security policy model (SPM) to define the access control relationships that service composition must comply with in the application plane, then SPM model is transformed into the low-level security policy model (RSPM) containing the information of SDN data plane, and RSPM model is uploaded into the SDN controlle…
The enforcement of sensitive policies in untrusted environments is still an open challenge for policy-based systems. On the one hand, taking any appropriate security decision requires access to these policies. On the other hand, if such access is allowed in an untrusted environment then confidential information might be leaked by the policies. The key challenge is how to enforce sensitive policies and protect content in untrusted environments. In the context of untrusted environments, we mainly distinguish between outsourced and distributed environments. The most attractive paradigms concernin…
Today's mobile devices sense, collect, and store huge amounts of personal information, which users share with family and friends through a wide range of applications. Once users give applications access to their data, they must implicitly trust that the apps correctly maintain data privacy. As we know from both experience and all-too-frequent press articles, that trust is often misplaced. While users do not trust applications, they do trust their mobile devices and operating systems. Unfortunately, sharing applications are not limited to mobile clients but must also run on cloud services to sh…
We present an iterative algorithm for enforcing policies represented in a first-order logic, which can, in particular, express all transmission-related clauses in the HIPAA Privacy Rule. The logic has three features that raise challenges for enforcement --- uninterpreted predicates (used to model subjective concepts in privacy policies), real-time temporal properties, and quantification over infinite domains (such as the set of messages containing personal information). The algorithm operates over audit logs that are inherently incomplete and evolve over time. In each iteration, the algorithm …
For security and privacy management and enforcement purposes, various policy languages have been presented. We give an overview on 27 security and privacy policy languages and present a categorization framework for policy languages. We show how the current policy languages are represented in the framework and summarize our interpretation. We show up identified gaps and motivate for the adoption of policy languages for the specification of privacy-utility trade-off policies.
Recent data protection regulations (such as GDPR and CCPA) grant consumers various rights, including the right to access, modify or delete any personal information collected about them (and retained) by a service provider. To exercise these rights, one must submit a verifiable consumer request proving that the collected data indeed pertains to them. This action is straightforward for consumers with active accounts with a service provider at the time of data collection, since they can use standard (e.g., password-based) means of authentication to validate their requests. However, a major conund…
Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）；University of Toronto（多伦多大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Los Angeles（加州大学洛杉矶分校）
Privacy analysis is critical but also a time-consuming and tedious task. We present a formalization which eases designing and auditing high-level privacy properties of software architectures. It is incorporated into a larger policy analysis and verification framework and enables the assessment of commonly accepted data protection goals of privacy. The formalization is based on static taint analysis and makes flow and processing of privacy-critical data explicit, globally as well as on the level of individual data subjects. Formally, we show equivalence to traditional label-based information fl…
Online websites use cookie notices to elicit consent from the users, as required by recent privacy regulations like the GDPR and the CCPA. Prior work has shown that these notices use dark patterns to manipulate users into making website-friendly choices which put users' privacy at risk. In this work, we develop CookieEnforcer, a new system for automatically discovering cookie notices and deciding on the options that result in disabling all non-essential cookies. In order to achieve this, we first build an automatic cookie notice detector that utilizes the rendering pattern of the HTML elements…
Privacy risk assessments aim to analyze and quantify the privacy risks associated with new systems. As such, they are critically important in ensuring that adequate privacy protections are built in. However, current methods to quantify privacy risk rely heavily on experienced analysts picking the "correct" risk level on e.g. a five-point scale. In this paper, we argue that a more scientific quantification of privacy risk increases accuracy and reliability and can thus make it easier to build privacy-friendly systems. We discuss how the impact and likelihood of privacy violations can be decompo…
We consider the privacy implications of public release of a de-identified dataset of Opal card transactions. The data was recently published at https://opendata.transport.nsw.gov.au/dataset/opal-tap-on-and-tap-off. It consists of tap-on and tap-off counts for NSW's four modes of public transport, collected over two separate week-long periods. The data has been further treated to improve privacy by removing small counts, aggregating some stops and routes, and perturbing the counts. This is a summary of our findings.
The question of how government agencies can acquire actionable, useful information about legitimate but unknown targets without intruding upon the electronic activity of innocent parties is extremely important. We address this question by providing experimental evidence that actionable, useful information can indeed be obtained in a manner that preserves the privacy of innocent parties and that holds government agencies accountable. In particular, we present practical, privacy-preserving protocols for two operations that law-enforcement and intelligence agencies have used effectively: set inte…
We increasingly live in a world where there is a balance between the rights to privacy and the requirements for consent, and the rights of society to protect itself. Within this world, there is an ever-increasing requirement to protect the identities involved within financial transactions, but this makes things increasingly difficult for law enforcement agencies, especially in terms of financial fraud and money laundering. This paper reviews the state-of-the-art in terms of the methods of privacy that are being used within cryptocurrency transactions, and in the challenges that law enforcement…
China is currently the country with the largest number of Android smartphone users. We use a combination of static and dynamic code analysis techniques to study the data transmitted by the preinstalled system apps on Android smartphones from three of the most popular vendors in China. We find that an alarming number of preinstalled system, vendor and third-party apps are granted dangerous privileges. Through traffic analysis, we find these packages transmit to many third-party domains privacy sensitive information related to the user's device (persistent identifiers), geolocation (GPS coordina…
Mounting privacy legislation calls for the preservation of privacy in speech technology, though solutions are gravely lacking. While evaluation campaigns are long-proven tools to drive progress, the need to consider a privacy adversary implies that traditional approaches to evaluation must be adapted to the assessment of privacy and privacy preservation solutions. This paper presents the first step in this direction: metrics. We introduce the zero evidence biometric recognition assessment (ZEBRA) framework and propose two new privacy metrics. They measure the average level of privacy preservat…
Privacy policies often place requirements on the purposes for which a governed entity may use personal information. For example, regulations, such as HIPAA, require that hospital employees use medical information for only certain purposes, such as treatment. Thus, using formal or automated methods for enforcing privacy policies requires a semantics of purpose requirements to determine whether an action is for a purpose or not. We provide such a semantics using a formalism based on planning. We model planning using a modified version of Markov Decision Processes, which exclude redundant actions…
Information-Centric Networking (ICN) is a new networking paradigm, which replaces the widely used host-centric networking paradigm in communication networks (e.g., Internet, mobile ad hoc networks) with an information-centric paradigm, which prioritizes the delivery of named content, oblivious of the contents origin. Content and client security are more intrinsic in the ICN paradigm versus the current host centric paradigm where they have been instrumented as an after thought. By design, the ICN paradigm inherently supports several security and privacy features, such as provenance and identity…
Privacy and data protection have become more and more important in recent years since an increasing number of enterprises and startups are harvesting personal data as a part of their business model. One central requirement of the GDPR is the implementation of a data protection impact assessment for privacy critical systems. However, the law does not dictate or recommend the use of any particular framework. In this paper we compare different data protection impact assessment frameworks. We have developed a comparison and evaluation methodology and applied this to three popular impact assessment…
Information security and privacy in the healthcare sector is an issue of growing importance. The adoption of digital patient records, increased regulation, provider consolidation and the increasing need for information exchange between patients, providers and payers, all point towards the need for better information security. We critically survey the literature on information security and privacy in healthcare, published in information systems journals as well as many other related disciplines including health informatics, public health, law, medicine, and the trade press and industry reports
Multi-PA: A Multi-perspective Benchmark on Privacy Assessment for Large Vision-Language Models
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Online Automated Privacy Policy Generators (APPGs) are tools used by app developers to quickly create app privacy policies which are required by privacy regulations to be incorporated to each mobile app. The creation of these tools brings convenience to app developers; however, the quality of these tools puts developers and stakeholders at legal risk. In this paper, we conduct an empirical study to assess the quality of online APPGs. We analyze the completeness of privacy policies, determine what categories and items should be covered in a complete privacy policy, and conduct APPG assessment w…
Security is one of the major concerns of modern communication systems. Users demand a secure communication environment that provides privacy to the people while they are sharing messages to anyone. Privacy is a prime concern nowadays. This paper aims to provide an optimal platform for communication between the sender and the receiver. This prototype designed in the paper will provide a better anonymous path for routing messages. It will ensure ones full privacy while he or she is using this system for communication. As the proposed system provides a secure communication environment, it is supp…
Web-based chatbots provide website owners with the benefits of increased sales, immediate response to their customers, and insight into customer behaviour. While Web-based chatbots are getting popular, they have not received much scrutiny from security researchers. The benefits to owners come at the cost of users' privacy and security. Vulnerabilities, such as tracking cookies and third-party domains, can be hidden in the chatbot's iFrame script. This paper presents a large-scale analysis of five Web-based chatbots among the top 1-million Alexa websites. Through our crawler tool, we identify t…
It is well known that users on open blockchains are tracked by an industry providing services to governments, law enforcement, secret services, and alike. While most blockchains do not protect their users' privacy and allow external observers to link transactions and addresses, a growing research interest attempts to design add-on privacy solutions to help users regain their privacy on non-private blockchains. In this work, we propose to our knowledge the first censorship resilient mixer, which can reward its users in a privacy-preserving manner for participating in the system. Increasing the …
Next-generation wireless networks like 5G promise faster speed, shorter latency, and the ability to connect more devices. Such benefits are set to make drastic changes to the future society, empowering smart cities, enabling autonomous cars, enhancing business processes, changing consumer behaviors, etc. In the financial industry, banks evaluate the deployment of Internet of Things (IoT) technologies and edge computing for better customer engagement, e.g., mobile branches on a vehicle, micro-ATM, self-service digital panel, etc. One of the trends is breaking down monolithic business applicatio…
Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data holders that spans multiple institutional and legislative domains to securely compute GWAS results. However, these algorithms have limited applicability, since they still require a centralized instance to decide whether GWAS results can be safely disclosed, which is in violation to privacy regulations, such as GDPR. In this work, we introduce GenDPR, a dist…
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes at…
PriEval-Protect: A Unified Framework for Privacy Evaluation and Protection in Healthcare Systems
National Institute of Applied Science and Technology INSAT（国立应用科学与技术学院）；Efrei Research Lab, Efrei, Paris-Panthéon-Assas University（巴黎-潘特翁-阿萨斯大学埃弗雷伊研究实验室）；LARIA Research Lab, National School Of Computer Science ENSI（国立计算机科学学院拉里亚研究实验室）
In this paper we propose a formal framework for studying privacy in information systems. The proposal follows a two-axes schema where the first axis considers privacy as a taxonomy of rights and the second axis involves the ways an information system stores and manipulates information. We develop a correspondence between the above schema and an associated model of computation. In particular, we propose the \Pcalc, a calculus based on the $π$-calculus with groups extended with constructs for reasoning about private data. The privacy requirements of an information system are captured via a priva…
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties
Considerable concerns exist over privacy on social networks, and huge debates persist about how to extend the artifacts users need to effectively protect their rights to privacy. While many interesting ideas have been proposed, no single approach appears to be comprehensive enough to be the front runner. In this paper, we propose a comprehensive and novel reference conceptual model for privacy in constantly evolving social networks and establish its novelty by briefly contrasting it with contemporary research. We also present the contours of a possible query language that we can develop with d…
The rapid spread of COVID-19 has made manual contact tracing difficult. Thus, various public health authorities have experimented with automatic contact tracing using mobile applications (or "apps"). These apps, however, have raised security and privacy concerns. In this paper, we propose an automated security and privacy assessment tool, COVIDGUARDIAN, which combines identification and analysis of Personal Identification Information (PII), static program analysis and data flow analysis, to determine security and privacy weaknesses. Furthermore, in light of our findings, we undertake a user st…
