All major web browsers support extensions to provide additional functionalities and enhance users’ browsing experience while the extensions can access and collect users’ data during their web browsing. Although the web extensions inform users of their data practices via multiple forms of notices, prior work has overlooked the critical gap between the actual data practices and the published privacy notices of browser extensions. To fill this gap, we propose ExtPrivA that automatically detects the inconsistencies between browser extensions’ data collection and their privacy disclosures. From the privacy policies and Dashboard disclosures, ExtPrivA extracts privacy statements to have a clear interpretation of the privacy practices of an extension. It emulates user interactions to trigger the extension’s functionalities and analyzes the initiators of network requests to accurately extract the users’ data transferred by the extension from the browser to external servers. Our end-to-end evaluation has shown ExtPrivA to detect inconsistencies between the privacy disclosures and data-collection behavior with an 85% precision. In a large-scale study of 47.2k extensions on the Chrome Web Store, we found 820 extensions with 1,290 flows that are inconsistent with their privacy statements. Even worse, we have found 525 pairs of contradictory privacy statements in the Dashboard disclosures and privacy policies of 360 extensions. These discrepancies between the privacy disclosures and the actual data-collection behavior are deemed as serious violations of the Store’s policies. Our findings highlight the critical issues in the privacy disclosures of browser extensions that potentially mislead, and even pose high privacy risks to, end-users.

Online trackers, such as advertising and analytics services, have provided users with choices to opt out of their tracking and data collection to mitigate the users' concerns about increased privacy risks. While opt-out choices of online services for the cookies placed on their own websites have been examined before, the choices provided by trackers for their third-party tracking services on publisher websites have been largely overlooked. There is no guarantee that a tracker's opt-out options would faithfully follow the statements in its privacy policy. To address this concern, we develop an automated framework, called OptOutCheck, that analyzes (in)consistencies between trackers' data practices and the opt-out choice statements in their privacy policies. We create sentence-level classifiers, which achieve ≥ 84.6% precision on previously-unseen statements, to extract the opt-out policies that state neither tracking nor data collection for opted-out users from trackers' privacy-policy documents. tOptOutCheck analyzes both tracker and publisher websites to detect opt-out buttons, perform the opt-out, and extract the data flows to the tracker servers after the user opts out. Finally, we formalize the opt-out policies and data flows to derive logical conditions to detect the inconsistencies. In a large-scale study of 2.9k popular trackers, OptOutCheck detected opt-out choices on 165 trackers and found 11 trackers who exhibited data practices inconsistent with their stated opt-out policies. Since inconsistencies are violations of the trackers' privacy policies and demonstrate data collection without user consent, they are likely to lose users' trust in the online trackers and trigger the necessity of an automatic auditing process.

While privacy laws and regulations require apps and services to disclose the purposes of their data collection to the users (i.e., why do they collect my data?), the data usage in an app's actual behavior does not always comply with the purposes stated in its privacy policy. Automated techniques have been proposed to analyze apps' privacy policies and their execution behavior, but they often overlooked the purposes of the apps' data collection, use and sharing. To mitigate this oversight, we propose PurPliance, an automated system that detects the inconsistencies between the data-usage purposes stated in a natural language privacy policy and those of the actual execution behavior of an Android app. PurPliance analyzes the predicate-argument structure of policy sentences and classifies the extracted purpose clauses into a taxonomy of data purposes. Purposes of actual data usage are inferred from network data traffic. We propose a formal model to represent and verify the data usage purposes in the extracted privacy statements and data flows to detect policy contradictions in a privacy policy and flow-to-policy inconsistencies between network data flows and privacy statements. Our evaluation results of end-to-end contradiction detection have shown PurPliance to improve detection precision from 19% to 95% and recall from 10% to 50% compared to a state-of-the-art method. Our analysis of 23.1k Android apps has also shown PurPliance to detect contradictions in 18.14% of privacy policies and flow-to-policy inconsistencies in 69.66% of apps, indicating the prevalence of inconsistencies of data practices in mobile apps.

Abstract Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score.

LibAS is a cross-platform framework to facilitate the rapid development of mobile acoustic sensing apps. It helps developers quickly realize their ideas by using a high-level Matlab script, and test them on various OS platforms, such as Android, iOS, Tizen, and Linux/Win. LibAS simplifies the development of acoustic sensing apps by hiding the platform-dependent details. For example, developers need not learn Objective-C/SWIFT or the audio buffer management in the CoreAudio framework when they want to implement acoustic sensing algorithms on an iPhone. Instead, developers only need to decide on the sensing signals and the callback function to handle each repetition of sensing signals. We have implemented apps covering three major acoustic sensing categories to demonstrate the benefits and simplicity of developing apps with LibAS. Our evaluation results show the adaptability of LibAS in supporting various acoustic sensing apps and tuning/improving their performance efficiently. Developers have reported that LibAS saves them a significant amount of time/effort and can reduce up to 90% lines of code in their acoustic sensing apps.

A next generation VLIW DSP Central Processing Unit (CPU) which has an integrated fixed point and floating point Instruction Set Architecture (ISA) is presented. It is designed to meet a 1.5 GHz core clock frequency in a 40nm process with aggressive area and power goals. In this paper, the benchmarking process and benefits of newly defined instructions such as complex matrix multiply is explained. Also, the CPU data path is described in detail, highlighting several novel micro-architecture features. Finally, our design methodology as well as verification methodology to ensure functional correctness utilizing formal equivalent verification is described.