Biblio

Any technique to ensure memory safety requires knowledge of (a) precise array bounds and (b) the data types accessed by memory load/store and pointer move instructions (called, owners) in the program. While this information can be effectively derived by compiler-level approaches much of this information may be lost during the compilation process and become unavailable to binary-level tools. In this work we conduct the first detailed study on how accurately can this information be extracted or reconstructed by current state-of-the-art static reverse engineering (RE) platforms for binaries compiled with and without debug symbol information. Furthermore, it is also unclear how the imprecision in array bounds and instruction owner information that is obtained by the RE tools impacts the ability of techniques to detect illegal memory accesses at run-time. We study this issue by designing, building, and deploying a novel binary-level technique to assess the properties and effectiveness of the information provided by the static RE algorithms in the first stage to guide the run-time instrumentation to detect illegal memory accesses in the decoupled second stage. Our work explores the limitations and challenges for static binary analysis tools to develop accurate binary-level techniques to detect memory errors.

Many consumers now rely on different forms of voice assistants, both stand-alone devices and those built into smartphones. Currently, these systems react to specific wake-words, such as "Alexa," "Siri," or "Ok Google." However, with advancements in natural language processing, the next generation of voice assistants could instead always listen to the acoustic environment and proactively provide services and recommendations based on conversations without being explicitly invoked. We refer to such devices as "always listening voice assistants" and explore expectations around their potential use. In this paper, we report on a 178-participant survey investigating the potential services people anticipate from such a device and how they feel about sharing their data for these purposes. Our findings reveal that participants can anticipate a wide range of services pertaining to a conversation; however, most of the services are very similar to those that existing voice assistants currently provide with explicit commands. Participants are more likely to consent to share a conversation when they do not find it sensitive, they are comfortable with the service and find it beneficial, and when they already own a stand-alone voice assistant. Based on our findings we discuss the privacy challenges in designing an always-listening voice assistant.

Despite corporate cyber intrusions attracting all the attention, privacy breaches that we, as ordinary users, should be worried about occur every day without any scrutiny. Smartphones, a household item, have inadvertently become a major enabler of privacy breaches. Smartphone platforms use permission systems to regulate access to sensitive resources. These permission systems, however, lack the ability to understand users’ privacy expectations leaving a significant gap between how permission models behave and how users would want the platform to protect their sensitive data. This dissertation provides an in-depth analysis of how users make privacy decisions in the context of Smartphones and how platforms can accommodate user’s privacy requirements systematically. We first performed a 36-person field study to quantify how often applications access protected resources when users are not expecting it. We found that when the application requesting the permission is running invisibly to the user, they are more likely to deny applications access to protected resources. At least 80% of our participants would have preferred to prevent at least one permission request. To explore the feasibility of predicting user’s privacy decisions based on their past decisions, we performed a longitudinal 131-person field study. Based on the data, we built a classifier to make privacy decisions on the user’s behalf by detecting when the context has changed and inferring privacy preferences based on the user’s past decisions. We showed that our approach can accurately predict users’ privacy decisions 96.8% of the time, which is an 80% reduction in error rate compared to current systems. Based on these findings, we developed a custom Android version with a contextually aware permission model. The new model guards resources based on user’s past decisions under similar contextual circumstances. We performed a 38-person field study to measure the efficiency and usability of the new permission model. Based on exit interviews and 5M data points, we found that the new system is effective in reducing the potential violations by 75%. Despite being significantly more restrictive over the default permission systems, participants did not find the new model to cause any usability issues in terms of application functionality.

We present a scalable dynamic analysis framework that allows for the automatic evaluation of the privacy behaviors of Android apps. We use our system to analyze mobile apps’ compliance with the Children’s Online Privacy Protection Act (COPPA), one of the few stringent privacy laws in the U.S. Based on our automated analysis of 5,855 of the most popular free children’s apps, we found that a majority are potentially in violation of COPPA, mainly due to their use of thirdparty SDKs. While many of these SDKs offer configuration options to respect COPPA by disabling tracking and behavioral advertising, our data suggest that a majority of apps either do not make use of these options or incorrectly propagate them across mediation SDKs. Worse, we observed that 19% of children’s apps collect identifiers or other personally identifiable information (PII) via SDKs whose terms of service outright prohibit their use in child-directed apps. Finally, we show that efforts by Google to limit tracking through the use of a resettable advertising ID have had little success: of the 3,454 apps that share the resettable ID with advertisers, 66% transmit other, non-resettable, persistent identifiers as well, negating any intended privacy-preserving properties of the advertising ID.

Intelligent voice assistants (IVAs) and other voice-enabled devices already form an integral component of the Internet of Things and will continue to grow in popularity. As their capabilities evolve, they will move beyond relying on the wake-words today’s IVAs use, engaging instead in continuous listening. Though potentially useful, the continuous recording and analysis of speech can pose a serious threat to individuals’ privacy. Ideally, users would be able to limit or control the types of information such devices have access to. But existing technical approaches are insufficient for enforcing any such restrictions. To begin formulating a solution, we develop a system- atic methodology for studying continuous-listening applications and survey architectural approaches to designing a system that enhances privacy while preserving the benefits of always-listening assistants.

Older adults (65+) are becoming primary users of emerging smart systems, especially in health care. However, these technologies are often not designed for older users and can pose serious privacy and security concerns due to their novelty, complexity, and propensity to collect and communicate vast amounts of sensitive information. Efforts to address such concerns must build on an in-depth understanding of older adults' perceptions and preferences about data privacy and security for these technologies, and accounting for variance in physical and cognitive abilities. In semi-structured interviews with 46 older adults, we identified a range of complex privacy and security attitudes and needs specific to this population, along with common threat models, misconceptions, and mitigation strategies. Our work adds depth to current models of how older adults' limited technical knowledge, experience, and age-related declines in ability amplify vulnerability to certain risks; we found that health, living situation, and finances play a notable role as well. We also found that older adults often experience usability issues or technical uncertainties in mitigating those risks -- and that managing privacy and security concerns frequently consists of limiting or avoiding technology use. We recommend educational approaches and usable technical protections that build on seniors' preferences.

Many of the emerging wide-area monitoring protection and control (WAMPAC) applications in modern electrical grids rely heavily on the availability and integrity of widespread phasor measurement unit (PMU) data. Therefore, it is critical to protect PMU networks against growing cyber-attacks and system faults. In this paper, we present a self-healing PMU network design that considers both power system observability and communication network characteristics. Our design utilizes centralized network control, such as the emerging software-defined networking (SDN) technology, to design resilient network self-healing algorithms against cyber-attacks. Upon detection of a cyber-attack, the PMU network can reconfigure itself to isolate compromised devices and re-route measurement
data with the goal of preserving the power system observability. We have developed a proof-of-concept system in a container-based network testbed using integer linear programming to solve a graphbased PMU system model.We also evaluate the system performance regarding the self-healing plan generation and installation using the IEEE 30-bus system.
 

Smart home automation and IoT promise to bring many advantages but they also expose their users to certain security and privacy vulnerabilities. For example, leaking the information about the absence of a person from home or the medicine somebody is taking may have serious security and privacy consequences for home users and potential legal implications for providers of home automation and IoT platforms. We envision that a new ecosystem within an existing smartphone ecosystem will be a suitable platform for distribution of apps for smart home and IoT devices. Android is increasingly becoming a popular platform for smart home and IoT devices and applications. Built-in security mechanisms in ecosystems such as Android have limitations that can be exploited by malicious apps to leak users’ sensitive data to unintended recipients. For instance, Android enforces that an app requires the Internet permissions in order to access a web server but it does not control which servers the app talks to or what data it shares with other apps. Therefore, sub-ecosystems that enforce additional fine-grained custom policies on top of existing policies of the smartphone ecosystems are necessary for smart home or IoT platforms. To this end, we have built a tool that enforces additional policies on inter-app interactions and permissions of Android apps. We have done preliminary testing of our tool on three proprietary apps developed by a future provider of a home automation platform. Our initial evaluation demonstrates that it is possible to develop mechanisms that allow definition and enforcement of custom security policies appropriate for ecosystems of the like smart home automation and IoT.

Platform as a Service (PaaS) provides middleware resources to cloud customers. As demand for PaaS services increases, so do concerns about the security of PaaS. This paper discusses principal PaaS security and integrity requirements, and vulnerabilities and the corresponding countermeasures. We consider three core cloud elements: multi-tenancy, isolation, and virtualization and how they relate to PaaS services and security trends and concerns such as user and resource isolation, side-channel vulnerabilities in multi-tenant environments, and protection of sensitive data

The simplest and purest practical object-oriented language designs today are seen in dynamically-typed languages, such as Smalltalk and Self. Static types, however, have potential benefits for productivity, security, and reasoning about programs. In this paper, we describe the design of Wyvern, a statically typed, pure object-oriented language that attempts to retain much of the simplicity and expressiveness of these iconic designs. Our goals lead us to combine pure object-oriented and functional abstractions in a simple, typed setting. We present a foundational object-based language that we believe to be as close as one can get to simple typed lambda calculus while keeping object-orientation. We show how this foundational language can be translated to the typed lambda calculus via standard encodings. We then define a simple extension to this language that introduces classes and show that classes are no more than sugar for the foundational object-based language. Our future intention is to demonstrate that modules and other object-oriented features can be added to our language as not more than such syntactical extensions while keeping the object-oriented core as pure as possible. The design of Wyvern closely follows both historical and modern ideas about the essence of object-orientation, suggesting a new way to think about a minimal, practical, typed core language for objects.

Three years ago in this venue, Cook argued that in their essence, objects are what Reynolds called procedural data structures. His observation raises a natural question: if procedural data structures are the essence of objects, has this contributed to the empirical success of objects, and if so, how? This essay attempts to answer that question. After reviewing Cook's definition, I propose the term service abstractions to capture the essential nature of objects. This terminology emphasizes, following Kay, that objects are not primarily about representing and manipulating data, but are more about providing services in support of higher-level goals. Using examples taken from object-oriented frameworks, I illustrate the unique design leverage that service abstractions provide: the ability to define abstractions that can be extended, and whose extensions are interoperable in a first-class way. The essay argues that the form of interoperable extension supported by service abstractions is essential to modern software: many modern frameworks and ecosystems could not have been built without service abstractions. In this sense, the success of objects was not a coincidence: it was an inevitable consequence of their service abstraction nature.

Researchers interested in security often wish to introduce new primitives into a language. Extensible languages hold promise in such scenarios, but only if the extension mechanism is sufficiently safe and expressive. This paper describes several modifications to an extensible language motivated by end-to-end security concerns.

Access policies are hard to express in existing programming languages. However, their accurate expression is a prerequisite for many of today's applications. We propose a new language that uses classes, first-class relationships, and first-class states to express access policies in a more declarative and fine-grained way than existing solutions allow.

Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.