Database as a service (DBaaS) in cloud raises severe concern in terms of data security. Storing data in encrypted form may confirm data confidentiality. But, database processing cannot be supported in this encrypted form. Theoretically, homomorphic encryption is a solution to support direct encrypted data processing. In this work, we highlight one of the major challenges of FHE- encrypted query processing that demands huge data transfer requirements from cloud to client for final decryption at the end of SQL query execution. We show in light of Chosen Plaintext Attack (CPA) that in spite of performing conditional filtering through SQL queries over encrypted databases, the size of the resultant dataset cannot be less than the original size of the database. In this work, we make an effort to propose a new encrypted query processing framework termed asSHELDBwhich supports client storage compatibility and low communication overhead using block-wise final result transmission from cloud to client by extending the concept of TOP operator implementation in standard SQL. However, it is to be noted that realization of such optimization is not straightforward because of circuit-based implementation requirements with FHE gates. Our experimental demonstration shows that the proposed framework is capable of executing all TPC-C standard SQL queries with the aid of 8-core parallel processing within$\sim 12.65$minutes for an encrypted database of$768 \times 9$size with 16-bits elements each. Though the computation time is linear with the number of rows, we have explored map-reduce type parallel processing techniques to reduce the timing requirements for databases with larger rows. Consequently, our new query processing framework reduces the communication overhead from m to$\delta k$rows ($1 \leq \delta \leq block$) where the encrypted database contains m rows,$\delta$is the number of blocks to be transmitted each time with$k$= ($m/block$) rows. In spite of$k$being a controllable parameter according to client storage and$\delta$is dependent on the query parameters, final security analysis explains why the proposed technique is general database attack-resistant.

In public and shared platforms where security is paramount, encrypted database storage and processing have become a key research priority. However, traditionally encrypted databases do not support direct computation of relational queries, and hence, those types of query processing are infeasible without intermediate decryption. To achieve end-to-end encrypted computation, such databases prefer fully homomorphic encryption (FHE), which demands circuit-based representation of any algorithm. In our work, along with other encrypted SQL operations, we analyze SQL join and show straightforward implementation, which incurs huge performance overhead. Not only in performance, number of ciphertext expansions is also huge in this case, which can trigger a sharp rise in performance overhead in encrypted domain. To address these challenges, we propose a lightweight solution only for joining the encrypted databases using pair-wise traditional column encryption with the introduction of primary and foreign keys. Then, we highlight how to perform homomorphic operations over that encrypted joining result. Our implementation shows 89.34% improvement compared to straightforward FHE-joining. Consequently, we demonstrate memory overhead reduction ∼13%.

Cloud Computing is a promising solution in distributed internet computing for IT and scientific research. However, data storage and computing in the cloud domain raise added questions in terms of security. Data encrypted with traditional encryption schemes may confirm confidentiality, but computation in cloud domain becomes infeasible. This paper focuses on designing an encrypted database considering homomorphic encryption (HE) as an underlying scheme so that query execution is carried out on encrypted version of the database without any need of intermediate decryption. Existing encrypted databases are either based on partial HE or deterministic HE to achieve practical performance; hence, they are either limited in terms of types of query execution or prone to known attacks. To mitigate these issues, we explore the practical challenges of a fully homomorphic encryption (FHE)-based database design. FHE theoretically promises to perform arbitrary operations on encrypted data. However, realizing any algorithm in homomorphic domain requires a circuit-based representation of that specific algorithm, which is a non-trivial task. In this work, we explore the practical challenges of FHE database design, mostly in the case of multi-user organizational scenarios, and propose a scheme for secure modification or conditional update of the encrypted database. Moreover, when an organization outsources a database to the cloud, a single FHE key is used to encrypt all columns of the database. However, all users(or employees) of the same organization should not have equal read and write access permission to the whole database. In this work, we propose an architecture to apply Attribute-Based Access Control (ABAC) on FHE databases with minimum overhead in terms of performance and storage. We propose required changes in registration, login, and user revocation phases for our scheme to perform conditional SQL query processing on FHE encrypted database (EDB). Our proposed framework is capable of performing end-to-end encrypted conditional UPDATE with suitable access control within 17 minutes on a multi-core processor platform for 769 rows and 9 columns of database size with 16-bit size of data. To the best of our knowledge, our proposed technique is the first one in literature to support arbitrary secure encrypted SQL query execution with suitable access control.

Commuter comfort in cab rides affects driver rating as well as the reputation of ride-hailing firms like Uber/Lyft. Existing research has revealed that commuter comfort not only varies at a personalized level but also is perceived differently on different trips for the same commuter. Furthermore, there are several factors, including driving behavior and driving environment, affecting the perception of comfort. Automatically extracting the perceived comfort level of a commuter due to the impact of the driving behavior is crucial for a timely feedback to the drivers, which can help them to meet the commuter’s satisfaction. In light of this, we surveyed around 200 commuters who usually take such cab rides and obtained a set of features that impact comfort during cab rides. Following this, we develop a system Ridergo which collects smartphone sensor data from a commuter, extracts the spatial time series feature from the data, and then computes the level of commuter comfort on a five-point scale with respect to the driving. Ridergo uses a Hierarchical Temporal Memory model-based approach to observe anomalies in the feature distribution and then trains a multi-task learning-based neural network model to obtain the comfort level of the commuter at a personalized level. The model also intelligently queries the commuter to add new data points to the available dataset and, in turn, improve itself over periodic training. Evaluation of Ridergo on 30 participants shows that the system could provide efficient comfort score with high accuracy when the driving impacts the perceived comfort.