Biblio

Multi-factor authentication (MFA) systems are being deployed for user authentication in online and personal device systems, whereas physical spaces mostly rely on single-factor authentication; examples are entering offices and homes, airport security, and classroom attendance. The Internet of Things (IoT) growth and market interest has created a diverse set of low-cost and flexible sensors and actuators that can be used for MFA. However, combining multiple authentication factors in a physical space adds several challenges, such as complex deployment, reduced usability, and increased energy consumption. We introduce MAFIA (Multi-layered Architecture For IoT-based Authentication), a novel architecture for co-located user authentication composed of multiple IoT devices. In MAFIA, we improve the security of physical spaces while considering usability, privacy, energy consumption, and deployment complexity. MAFIA is composed of three layers that define specific purposes for devices, guiding developers in the authentication design while providing a clear understanding of the trade-offs for different configurations. We describe a case study for an Automated Classroom Attendance System, where we evaluated three distinct types of authentication setups and showed that the most secure setup had a greater usability penalty, while the other two setups had similar attributes in terms of security, privacy, complexity, and usability but varied highly in their energy consumption.

With the rising popularity of file-sharing services such as Google Drive and Dropbox in the workflows of individuals and corporations alike, the protection of client-outsourced data from unauthorized access or tampering remains a major security concern. Existing cryptographic solutions to this problem typically require server-side support, involve non-trivial key management on the part of users, and suffer from severe re-encryption penalties upon access revocations. This combination of performance overheads and management burdens makes this class of solutions undesirable in situations where performant, platform-agnostic, dynamic sharing of user content is required. We present NEXUS, a stackable filesystem that leverages trusted hardware to provide confidentiality and integrity for user files stored on untrusted platforms. NEXUS is explicitly designed to balance security, portability, and performance: it supports dynamic sharing of protected volumes on any platform exposing a file access API without requiring server-side support, enables the use of fine-grained access control policies to allow for selective sharing, and avoids the key revocation and file re-encryption overheads associated with other cryptographic approaches to access control. This combination of features is made possible by the use of a client-side Intel SGX enclave that is used to protect and share NEXUS volumes, ensuring that cryptographic keys never leave enclave memory and obviating the need to reencrypt files upon revocation of access rights. We implemented a NEXUS prototype that runs on top of the AFS filesystem and show that it incurs ×2 overhead for a variety of common file and database operations.

Major online messaging services such as Facebook Messenger and WhatsApp are starting to provide users with real-time information about when people read their messages, while useful, the feature has the potential to negatively impact privacy as well as cause concern over access to self. We report on two surveys using Mechanical Turk which looked at senders' (N=402\vphantom\\ use of and reactions to the `message seen' feature, and recipients' (N=316) privacy and signaling behaviors in the face of such visibility. Our findings indicate that senders experience a range of emotions when their message is not read, or is read but not answered immediately. Recipients also engage in various signaling behaviors in the face of visibility by both replying or not replying immediately.

With data becoming available in larger quantities and at higher rates, new data processing paradigms have been proposed to handle high-volume, fast-moving data. Data Stream Processing is one such paradigm wherein transient data streams flow through sets of continuous queries, only returning results when data is of interest to the querier. To avoid the large costs associated with maintaining the infrastructure required for processing these data streams, many companies will outsource their computation to third-party cloud services. This outsourcing, however, can lead to private data being accessed by parties that a data provider may not trust. The literature offers solutions to this confidentiality and access control problem but they have fallen short of providing a complete solution to these problems, due to either immense overheads or trust requirements placed on these third-party services. To address these issues, we have developed PolyStream, an enhancement to existing data stream management systems that enables data providers to specify attribute-based access control policies that are cryptographically enforced while simultaneously allowing many types of in-network data processing. We detail the access control models and mechanisms used by PolyStream, and describe a novel use of security punctuations that enables flexible, online policy management and key distribution. We detail how queries are submitted and executed using an unmodified Data Stream Management System, and show through an extensive evaluation that PolyStream yields a 550x performance gain versus the state-of-the-art system StreamForce in CODASPY 2014, while providing greater functionality to the querier.

With data becoming available in larger quantities and at higher rates, new data processing paradigms have been proposed to handle high-volume, fast-moving data. Data Stream Processing is one such paradigm wherein transient data streams flow through sets of continuous queries, only returning results when data is of interest to the querier. To avoid the large costs associated with maintaining the infrastructure required for processing these data streams, many companies will outsource their computation to third-party cloud services. This outsourcing, however, can lead to private data being accessed by parties that a data provider may not trust. The literature offers solutions to this confidentiality and access control problem but they have fallen short of providing a complete solution to these problems, due to either immense overheads or trust requirements placed on these third-party services. To address these issues, we have developed PolyStream, an enhancement to existing data stream management systems that enables data providers to specify attribute-based access control policies that are cryptographically enforced while simultaneously allowing many types of in-network data processing. We detail the access control models and mechanisms used by PolyStream, and describe a novel use of security punctuations that enables flexible, online policy management and key distribution. We detail how queries are submitted and executed using an unmodified Data Stream Management System, and show through an extensive evaluation that PolyStream yields a 550x performance gain versus the state-of-the-art system StreamForce in CODASPY 2014, while providing greater functionality to the querier.