An official website of the United States government
An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users' data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.
more »
« less
Building a Privacy-Preserving Smart Camera System
Abstract Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users’ knowledge or consent—violating the core tenet of user privacy. In this paper, we present CaCTUs , a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user ; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2 s at a frame rate of 10 fps and a resolution of 480 p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data.
more »
« less
- Award ID(s):
- 1900873
- PAR ID:
- 10321949
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2022
- Issue:
- 2
- ISSN:
- 2299-0984
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The transparency and privacy behavior of mobile browsers has remained widely unexplored by the research community. In fact, as opposed to regular Android apps, mobile browsers may present contradicting privacy behaviors. On the one end, they can have access to (and can expose) a unique combination of sensitive user data, from users’ browsing history to permission-protected personally identifiable information (PII) such as unique identifiers and geolocation. However, on the other end, they also are in a unique position to protect users’ privacy by limiting data sharing with other parties by implementing ad-blocking features. In this paper, we perform a comparative and empirical analysis on how hundreds of Android web browsers protect or expose user data during browsing sessions. To this end, we collect the largest dataset of Android browsers to date, from the Google Play Store and four Chinese app stores. Then, we developed a novel analysis pipeline that combines static and dynamic analysis methods to find a wide range of privacy-enhancing (e.g., ad-blocking) and privacy-harming behaviors (e.g., sending browsing histories to third parties, not validating TLS certificates, and exposing PII---including non-resettable identifiers---to third parties) across browsers. We find that various popular apps on both Google Play and Chinese stores have these privacy-harming behaviors, including apps that claim to be privacy-enhancing in their descriptions. Overall, our study not only provides new insights into important yet overlooked considerations for browsers’ adoption and transparency, but also that automatic app analysis systems (e.g., sandboxes) need context-specific analysis to reveal such privacy behaviors.more » « less
-
In the smart home landscape, there is an increasing trend of homeowners sharing device access outside their homes. This practice presents unique challenges in terms of security and privacy. In this study, we evaluated the co-management features in smart home management systems to investigate 1) how homeowners establish and authenticate shared users’ access, 2) the access control mechanisms, and 3) the management, monitoring, and revocation of access for shared devices. We conducted a systematic feature analysis of 11 Android and iOS mobile applications (“apps”) and 2 open-source platforms designed for smart home management. Our study revealed that most smart home systems adopt a centralized control model which necessitates shared users to utilize the primary app for device access, while providing diverse sharing mechanisms, such as email or phone invitations and unique codes, each presenting distinct security and privacy advantages. Moreover, we discovered a variety of access control options, ranging from full access to granular access control such as time-based restrictions which, while enhancing security and convenience, necessitate careful management to avoid user confusion. Additionally, our findings highlighted the prevalence of comprehensive methods for monitoring shared users’ access, with most systems providing detailed logs for added transparency and security, although there are some restrictions to safeguard homeowner privacy. Based on our findings, we recommend enhanced access control features to improve user experience in shared settings.more » « less
-
Specialized machine learning (ML) models tailored to users’ needs and requests are increasingly being deployed on smart devices with cameras, to provide personalized intelligent services taking advantage of camera data. However, two primary challenges hinder the training of such models: the lack of publicly available labeled data suitable for specialized tasks and the inaccessibility of labeled private data due to concerns about user privacy. To address these challenges, we propose a novel system SpinML, where the server generates customized Synthetic image data to Privately traIN a specialized ML model tailored to the user request, with the usage of only a few sanitized reference images from the user. SpinML offers users fine-grained, object-level control over the reference images, which allows user to trade between the privacy and utility of the generated synthetic data according to their privacy preferences. Through experiments on three specialized model training tasks, we demonstrate that our proposed system can enhance the perfor- mance of specialized models without compromising users’ privacy preferences.more » « less
-
Smart home devices are constantly exchanging data with a variety of remote endpoints. This data encompasses diverse information, from device operation and status to sensitive user information like behavioral usage patterns. However, there is a lack of transparency regarding where such data goes and with whom it is potentially shared. This paper investigates the diverse endpoints that smart home Internet-of-Things (IoT) devices contact to better understand and reason about the IoT backend infrastructure, thereby providing insights into potential data privacy risks. We analyze data from 5,413 users and 25,123 IoT devices using the IoT Inspector, an open-source application allowing users to monitor traffic from smart home devices on their networks. First, we develop semi-automated techniques to map remote endpoints to organizations and their business types to shed light on their potential relationships with IoT end products. We discover that IoT devices contact more third or support-party domains than first-party domains. We also see that the distribution of contacted endpoints varies based on the user's location and across vendors manufacturing similar functional devices, where some devices are more exposed to third parties than others. Our analysis also reveals the major organizations providing backend support for IoT smart devices and provides insights into the temporal evolution of cross-border data-sharing practices.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2478/popets-2022-0034
