Trace Oblivious Program Execution

The big data era has dramatically transformed our lives; however, security incidents such as data breaches can put sensitive data (e.g. photos, identities, genomes) at risk. To protect users' data privacy, there is a growing interest in building secure cloud computing systems, which keep sensitive data inputs hidden, even from computation providers. Conceptually, secure cloud computing systems leverage cryptographic techniques (e.g., secure multiparty computation) and trusted hardware (e.g. secure processors) to instantiate a “secure” abstract machine consisting of a CPU and encrypted memory, so that an adversary cannot learn information through either the computation within the CPU or the data in the memory. Unfortunately, evidence has shown that side channels (e.g. memory accesses, timing, and termination) in such a “secure” abstract machine may potentially leak highly sensitive information, including cryptographic keys that form the root of trust for the secure systems. This thesis broadly expands the investigation of a research direction called trace oblivious computation, where programming language techniques are employed to prevent side channel information leakage. We demonstrate the feasibility of trace oblivious computation, by formalizing and building several systems, including GhostRider, which is a hardware-software co-design to provide a hardware-based trace oblivious computing solution, SCVM, which is an automatic RAM-model secure computation system, and ObliVM, which is a programming framework to facilitate programmers to develop applications. All of these systems enjoy formal security guarantees while demonstrating a better performance than prior systems, by one to several orders of magnitude.

ADVERTISEMENT ALLOCATION AND TRUST MECHANISMS DESIGN IN SOCIAL NETWORKS

Social network sites (SNS), such as Facebook, Google+ and Twitter, have attracted hundreds of millions of users daily since their appearance. Within SNS, users connect to each other, express their identity, disseminate information and form cooperation by interacting with their connected peers. The increasing popularity and ubiquity of SNS usage and the invaluable user behaviors and connections give birth to many applications and business models. We look into several important problems within the social network ecosystem. The first one is the SNS advertisement allocation problem. The other two are related to trust mechanisms design in social network setting, including local trust inference and global trust evaluation. In SNS advertising, we study the problem of advertisement allocation from the ad platform's angle, and discuss its differences with the advertising model in the search engine setting. By leveraging the connection between social networks and hyperbolic geometry, we propose to solve the problem via approximation using hyperbolic embedding and convex optimization. A hyperbolic embedding method, \hcm, is designed for the SNS ad allocation problem, and several components are introduced to realize the optimization formulation. We show the advantages of our new approach in solving the problem compared to the baseline integer programming (IP) formulation. In studying the problem of trust mechanisms in social networks, we consider the existence of distrust (i.e. negative trust) relationships, and differentiate between the concept of local trust and global trust in social network setting. In the problem of local trust inference, we propose a 2-D trust model. Based on the model, we develop a semiring-based trust inference framework. In global trust evaluation, we consider a general setting with conflicting opinions, and propose a consensus-based approach to solve the complex problem in signed trust networks.

When Love Becomes Hate(?): The Interplay Between Consumer-Brand Relationships and Crisis Situations

This study had three purposes. First, it aimed to re-conceptualize organization-public relationships (OPRs) in public relations and crisis communication. This OPR re-conceptualization helps find out when the OPR buffering effect or the OPR love-becomes-hate effect happens. Second, it aimed to examine how consumer emotions are influenced by OPRs and influence consumer behavioral intentions. Third, it aimed to address the current problematic operationalization of the concept of consumer. Three pilot studies and one main study were conducted. Apple and Whole Foods were the two brands examined. One crisis that undermined the self-defining attributes shared between the brand and its consumers and another crisis that did not were examined for each brand. Almost 500 Apple consumers and 400 Whole Foods consumers provided usable questionnaires. This study had several major findings. First, non-identifying relationship and identifying relationship were different constructs. Moreover, trust, satisfaction, and commitment were not conceptually separate dimensions of OPRs. Second, the non-identifying relationships offered buffering effects by increasing positive attitudes and tempering anger and disappointment. The identifying relationships primarily offered the love-becomes-hate effects by increasing anger and disappointment. Third, if the crisis was relevant to consumers’ daily lives, brand response strategies were less effective at mitigating consumer negative reactions. Moreover, apology-compensation-reminder strategy was more effective compared to no-comment strategy. However, the apology-compensation-reminder strategy was no more effective than other strategies as long as brands compensate to the victims. Identifying relationships increased the effectiveness of response strategies. If the crisis did not undermine the self-defining attributes shared between consumers and brands, the response strategies worked even better. This study contributes to crisis communication research in multiple ways. First, it advances the OPR conceptualization by demonstrating that non-identifying relationship and identifying relationship are different concepts. More importantly, it advances the theory building of OPRs’ influences on crises by finding out when the buffering effect and the love-becomes-hate effect happen. Second, it adds to emotion research by demonstrating that strong OPRs can lead to negative emotions and positive emotions can have negative behavioral consequences on organizations. Third, the precise operationalization of the concept of consumer gives more insights about consumer reactions to crises.