SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac

Extending the Bounded Rationality Model: The Distributed Cognition Approach

The way Simon, and the major part of the scholars, presented and used bounded rationality directly refers to human computational capabilities (or “brute-force”). Despite its broad powers of explanation, some problems arise when taking into account the way the human cognitive system really works. In order to avoid these problems, we present an alternative model of rationality, where computation plays only a part, together with the implemented role of external resources, emotional and other non-strictly-rational variables.bounded rationality, distributed cognition, external resources, decision-making, problem solving, emotions

Artificial Intelligence and Drug Innovation

We study how artificial intelligence (AI) can influence the drug development process in the global pharmaceutical industry. Despite considerable effort made in developing drugs, pharmaceutical firms experience declines in novelty for drugs they produced. As AI becomes an important general purpose technology (GPT), it could be used to address some known challenges in the drug development process. Using two large-scale datasets that contain detailed historical records of global drug development and patents, we identify AI-related patents to approximate firms’ AI capabilities and construct a relatively new similarity-based metric to measure drug novelty based on their chemical structure. We find that AI can primarily affect the earliest stage in drug discovery when tasks are heavily dependent on automatic data processing and reasoning. However, it may not necessarily help with the more expensive and risky clinical trial stages that require substantial human engagements and interventions. Additionally, AI can facilitate the development for drugs at the medium level of chemical novelty more than at the extreme ends of the spectrum. Our study sheds light on the understanding of the roles and limitations modern technology can have on drug development, one of the most complex innovation processes in the world