Quadrature spatial modulation (QSM) exploits the in-phase and quadrature antenna dimensions to increase the number of spatial constellation points. In this paper, an improved version of QSM, which is naturally called improved quadrature spatial modulation (IQSM), is proposed. The main feature of the proposed scheme is to send a second constellation symbol over the in-phase and quadrature antenna dimensions. A significant performance advantage of the proposed scheme is realized at the cost of a slight increase in the number of radio-frequency (RF) chains. Performance comparisons with the most recent spatial modulation schemes confirm the advantage of the proposed IQSM. A theoretical upper bound of the bit error rate is also given and shown to be very tight at high signal-to-noise ratios.

The Blind Seer system (Oakland 2014) is an efficient and scalable DBMS that affords both client query privacy and server data protection. It also provides the ability to enforce authorization policies on the system, restricting client's queries while maintaining the privacy of both query and policy. Blind Seer supports a rich query set, including arbitrary boolean formulas, and is provably secure with respect to a controlled amount of search pattern leakage. No other system to date achieves this tradeoff of performance, generality, and provable privacy. A major shortcoming of Blind Seer is its reliance on semi-honest security, particularly for access control and data protection. A malicious client could easily cheat the query authorization policy and obtain any database records satisfying any query of its choice, thus violating basic security features of any standard DBMS. In sum, Blind Seer offers additional privacy to a client, but sacrifices a basic security tenet of DBMS. In the present work, we completely resolve the issue of a malicious client. We show how to achieve robust access control and data protection in Blind Seer with virtually no added cost to performance or privacy. Our approach also involves a novel technique for a semi-private function secure function evaluation (SPF-SFE) that may have independent applications. We fully implement our solution and report on its performance.

Query privacy in secure DBMS is an important feature, although rarely formally considered outside the theoretical community. Because of the high overheads of guaranteeing privacy in complex queries, almost all previous works addressing practical applications consider limited queries (e.g., just keyword search), or provide a weak guarantee of privacy. In this work, we address a major open problem in private DB: efficient sub linear search for arbitrary Boolean queries. We consider scalable DBMS with provable security for all parties, including protection of the data from both server (who stores encrypted data) and client (who searches it), as well as protection of the query, and access control for the query. We design, build, and evaluate the performance of a rich DBMS system, suitable for real-world deployment on today medium-to large-scale DBs. On a modern server, we are able to query a formula over 10TB, 100M-record DB, with 70 searchable index terms per DB row, in time comparable to (insecure) MySQL (many practical queries can be privately executed with work 1.2-3 times slower than MySQL, although some queries are costlier). We support a rich query set, including searching on arbitrary boolean formulas on keywords and ranges, support for stemming, and free keyword searches over text fields. We identify and permit a reasonable and controlled amount of leakage, proving that no further leakage is possible. In particular, we allow leakage of some search pattern information, but protect the query and data, provide a high level of privacy for individual terms in the executed search formula, and hide the difference between a query that returned no results and a query that returned a very small result set. We also support private and complex access policies, integrated in the search process so that a query with empty result set and a query that fails the policy are hard to tell apart.

Encrypted search --- performing queries on protected data --- has been explored in the past; however, its inherent inefficiency has raised questions of practicality. Here, we focus on improving the performance and extending its functionality enough to make it practical. We do this by optimizing the system, and by stepping back from the goal of achieving maximal privacy guarantees in an encrypted search scenario and consider efficiency and functionality as priorities.

An electronic payment system ideally should provide security, anonymity, fairness, transferability and scalability. Existing payment schemes often lack either anonymity or scalability. In this paper we propose WhoPay, a peer-topeer payment system that provides all the above properties. For anonymity, we represent coins with public keys; for scalability, we distribute coin transfer load across all peers, rather than rely on a central entity such as the broker. This basic version of WhoPay is as secure and scalable as existing peer-to-peer payment schemes, while providing a much higher level of user anonymity. We also introduce the idea of real-time double spending detection by making use of distributed hash tables (DHT). Simulation results show that the majority of the system load is handled by the peers under typical peer availability, indicating that WhoPay should scale well.

Stealth malware programs that silently infect enterprise and consumer machines are becoming a major threat to the future of the Internet. Resource hiding is a powerful stealth technique commonly used by malware to evade detection by computer users and anti-malware scanners. In this paper, we focus on a subclass of malware, termed "ghostware", which hide files, configuration settings, processes, and loaded modules from the operating system's query and enumeration application programming interfaces (APIs). Instead of targeting individual stealth implementations, we describe a systematic framework for detecting multiple types of hidden resources by leveraging the hiding behavior as a detection mechanism. Specifically, we adopt a cross-view diff-based approach to ghostware detection by comparing a high-level infected scan with a low-level clean scan and alternatively comparing an inside-the-box infected scan with an outside-the-box clean scan. We describe the design and implementation of the Strider GhostBuster tool and demonstrate its efficiency and effectiveness in detecting resources hidden by real-world malware such as rootkits, Trojans, and key-loggers.