Index Advisor tools settle for sub-optimal index configurations based on greedy heuristics, owing to the computational hardness of index selection. We investigate here how this limitation can be addressed by leveraging the computing power offered by quantum platforms. Specifically, we present a hybrid Quantum-Classical Index Advisor that judiciously incorporates gate-based quantum computing within a classical index selection wrapper. Two distinct trade-offs between solution quality and computational complexity are considered. First, index selection is modeled as a Quadratic Unconstrained Binary Optimization problem and solved using the popular Quantum Approximate Optimization Algorithm. The obtained solution is approximate, like greedy, but significantly better in quality while incurring only O (log( L )) computations, where L is the total number of candidate configurations. Second, index selection is modeled as a fully enumerative search and solved using the seminal Grover Search algorithm. A novel quantum oracle is proposed that performs computations on data hosted in the relative phase of a quantum superposition state, and is encoded using only standard quantum gates. This approach identifies, with high probability, the optimal index configuration with computations. We have implemented these two designs using the Qiskit SDK and performed proof-of-concept evaluations on both simulation and hardware platforms. Substantive quality improvements, by a multiplicative factor of 1.5 to 2 and approaching optimality, are obtained as compared to a commercial database engine implementing a greedy approach. Moreover, their quantum resource requirements effectively scale linearly with problem size, an essential feature from a feasibility perspective.

Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can lead to data leaks. To handle this problem we present Knowledge and Learning-based Adaptable System for Sensitive InFormation Identification and Handling (KLASSIFI) which is an end to end system capable of identifying and redacting sensitive information present in diagnostic data. KLASSIFI is highly customizable, allowing it to be used for various different business use cases by simply changing the configuration. KLASSIFI ensures that the output file is useful by retaining the metadata which is used by various debugging tools. Various optimizations have been done to improve the performance of KLASSIFI. Empirical evaluation of KLASSIFI shows that it is able to process large files (128 GB) in 84 minutes and its performance scales linearly with varying factors. This points to practicability of KLASSIFI.

Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on third parties for storing and managing data. However, third parties are often not trusted to store plaintext personal and sensitive data; data encryption is widely adopted to protect against intentional and unintentional attempts to read personal/sensitive data. Traditional encryption schemes do not support operations over the ciphertexts and thus anonymizing encrypted datasets is not feasible with current approaches. This paper explores the feasibility and depth of implementing a privacy-preserving data publishing workflow over encrypted datasets leveraging on homomorphic encryption. We demonstrate how we can achieve uniqueness discovery, data masking, differential privacy and k-anonymity over encrypted data requiring zero knowledge about the original values. We prove that the security protocols followed by our approach provide strong guarantees against inference attacks. Finally, we experimentally demonstrate the performance of our data publishing workflow components.

Machine learning models deployed on the cloud are susceptible to several security threats including extraction attacks. Adversaries may abuse a model's prediction API to steal the model thus compromising model confidentiality, privacy of training data, and revenue from future query payments. This work introduces a model extraction monitor that quantifies the extraction status of models by continually observing the API query and response streams of users. We present two novel strategies that measure either the information gain or the coverage of the feature space spanned by user queries to estimate the learning rate of individual and colluding adversaries. Both approaches have low computational overhead and can easily be offered as services to model owners to warn them against state of the art extraction attacks. We demonstrate empirical performance results of these approaches for decision tree and neural network models using open source datasets and BigML MLaaS platform.

Prior solutions for securely handling SQL range predicates in outsourced Cloud-resident databases have primarily focused on passive attacks in the Honest-but-Curious adversarial model, where the server is only permitted to observe the encrypted query processing. We consider here a significantly more powerful adversary, wherein the server can launch an active attack by clandestinely issuing specific range queries via collusion with a few compromised clients. The security requirement in this environment is that data values from a plaintext domain of size N should not be leaked to within an interval of size $$H$$ . Unfortunately, all prior encryption schemes for range predicate evaluation are easily breached with only $$O(\log _2\psi )$$ range queries, where $$\psi = N{/}H$$ . To address this lacuna, we present SPLIT, a new encryption scheme where the adversary requires exponentially more— $${\mathbf{O}}(\psi )$$ —range queries to breach the interval constraint and can therefore be easily detected by standard auditing mechanisms. The novel aspect of SPLIT is that each value appearing in a range-sensitive column is first segmented into two parts. These segmented parts are then independently encrypted using a layered composition of a secure block cipher with the order-preserving encryption and prefix-preserving encryption schemes, and the resulting ciphertexts are stored in separate tables. At query processing time, range predicates are rewritten into an equivalent set of table-specific sub-range predicates, and the disjoint union of their results forms the query answer. A detailed evaluation of SPLIT on benchmark database queries indicates that its execution times are well within a factor of two of the corresponding plaintext times, testifying its efficiency in resisting active adversaries.