Balancing smartness and privacy for the Ambient Intelligence

Ambient Intelligence (AmI) will introduce large privacy risks. Stored context histories are vulnerable for unauthorized disclosure, thus unlimited storing of privacy-sensitive context data is not desirable from the privacy viewpoint. However, high quality and quantity of data enable smartness for the AmI, while less and coarse data benefit privacy. This raises a very important problem to the AmI, that is, how to balance the smartness and privacy requirements in an ambient world. In this article, we propose to give to donors the control over the life cycle of their context data, so that users themselves can balance their needs and wishes in terms of smartness and privacy

Trust-based model for privacy control in context aware systems

In context-aware systems, there is a high demand on providing privacy solutions to users when they are interacting and exchanging personal information. Privacy in this context encompasses reasoning about trust and risk involved in interactions between users. Trust, therefore, controls the amount of information that can be revealed, and risk analysis allows us to evaluate the expected benefit that would motivate users to participate in these interactions. In this paper, we propose a trust-based model for privacy control in context-aware systems based on incorporating trust and risk. Through this approach, it is clear how to reason about trust and risk in designing and implementing context-aware systems that provide mechanisms to protect users' privacy. Our approach also includes experiential learning mechanisms from past observations in reaching better decisions in future interactions. The outlined model in this paper serves as an attempt to solve the concerns of privacy control in context-aware systems. To validate this model, we are currently applying it on a context-aware system that tracks users' location. We hope to report on the performance evaluation and the experience of implementation in the near future

Context-Aware Generative Adversarial Privacy

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model, and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.Comment: Improved version of a paper accepted by Entropy Journal, Special Issue on Information Theory in Machine Learning and Data Scienc

Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

Using P3P in a web services-based context-aware application platform

This paper describes a proposal for a privacy control architecture to be applied in the WASP project. The WASP project aims to develop a context-aware service platform on top of 3G networks, using web services technology. The proposed privacy control architecture is based on the P3P privacy policy description standard defined by W3C. The paper identifies extensions to P3P and its associated preference expression language APPEL that are needed to operate in a context-aware environment

On trust and privacy in context-aware systems

Recent advances in networking, handheld computing and sensors technologies have led to the emergence of context-aware systems. The vast amounts of personal information collected by such systems has led to growing concerns about the privacy of their users. Users concerned about their private information are likely to refuse participation in such systems. Therefore, it is quite clear that for any context-aware system to be acceptable by the users, mechanisms for controlling access to personal information are a necessity. According to Alan Westin "privacy is the claim of individuals, groups, or institutions to determine for themselves when, how and to what extent information is communicated to others"1. Within this context we can classify users as either information owners or information receivers. It is also acknowledged that information owners are willing to disclose personal information if this disclosure is potentially beneficial. So, the acceptance of any context-aware system depends on the provision of mechanisms for fine-grained control of the disclosure of personal information incorporating an explicit notion of benefit

Secret charing vs. encryption-based techniques for privacy preserving data mining

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach

Automatic Detection of Vague Words and Sentences in Privacy Policies

Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However, privacy policies are often vague and people struggle to understand the content. Their opaqueness poses a significant challenge to both users and policy regulators. In this paper, we seek to identify vague content in privacy policies. We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection. In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. Our experimental results demonstrate the effectiveness of proposed approaches. Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.Comment: 10 page

A Framework for Analyzing and Comparing Privacy States

This article develops a framework for analyzing and comparing privacy and privacy protections across (inter alia) time, place, and polity and for examining factors that affect privacy and privacy protection. This framework provides a method to describe precisely aspects of privacy and context and a flexible vocabulary and notation for such descriptions and comparisons. Moreover, it links philosophical and conceptual work on privacy to social science and policy work and accommodates different conceptions of the nature and value of privacy. The article begins with an outline of the framework. It then refines the view by describing a hypothetical application. Finally, it applies the framework to a real‐world privacy issue—campaign finance disclosure laws in the United States and France. The article concludes with an argument that the framework offers important advantages to privacy scholarship and for privacy policy makers