A Practical and Secure Stateless Order Preserving Encryption for Outsourced Databases

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can\u27t be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice

Low-Resource Music Genre Classification with Advanced Neural Model Reprogramming

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.Comment: Submitted to ICASSP 2023. Some experimental results were reduced due to the space limit. The implementation will be available at https://github.com/biboamy/music-repr

De novo duplication of Xq22.1→q24 with a disruption of the NXF gene cluster in a mentally retarded woman with short stature and premature ovarian failure

AbstractObjectiveTo present molecular cytogenetic characterization of a de novo duplication of Xq22.1→q24 in a mentally retarded woman with short stature and premature ovarian failure.Materials and MethodsA 19-year-old woman presented with psychomotor retardation, developmental delay, mental retardation, short stature, low body weight, general muscle hypotonia, distal muscle hypotrophy of the lower extremities, elongated digits, scanty pubic and axillary hair, hypoplastic external female genitalia, and secondary amenorrhea but no clinical features of Pelizaeus-Merzbacher disease. Conventional cytogenetic analysis revealed a karyotype of 46,X,dup(X)(q22.1q24). Fluorescence in situ hybridization determined a direct duplication with a linear tandem orientation. Array comparative genomic hybridization demonstrated partial trisomy Xq [arr cgh Xq22.1q24 (101,490,234–119,070,188 bp)×3] with a 17.6-Mb duplication.ResultsThe duplicated region contained NXF2B, NXF4, NXF3, PLP1, and PGRMC1 genes. There was a disruption of the NXF gene cluster of Xcen-NXF5-NXF2-NXF2B-NXF4-NXF3-Xqter.ConclusionA duplication of Xq22.1→q24 with a disruption of the NXF gene cluster in female patients can be associated with clinical manifestations of mental retardation in addition to short stature and premature ovarian failure

Finite Control Set Model Predictive Control for an LCL-Filtered Grid-Tied Inverter with Full Status Estimations under Unbalanced Grid Voltage

This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with merely grid-injected current sensors for an inductance-capacitance-inductance (LCL)-filtered grid-tied inverter, which can obtain a sinusoidal grid-injected current whether three-phase grid voltages are balanced or not. Compared with the conventional FCS-MPC method, four compositions are added in the proposed FCS-MPC algorithm, where the grid voltage observer (GVO) and Luenberger observer are combined together to achieve full status estimations (including grid voltage, capacitor voltage, inverter-side current, and grid-injected current), while the sequence extractor and the reference generator are applied to eliminate the double frequency ripples of the active or reactive power, or the negative sequence component (NSC) of the grid-injected current caused by the unbalanced grid voltage. Simulation model and experimental platform are established to verify the effectiveness of the proposed FCS-MPC strategy, with full status estimations under both balanced and unbalanced grid voltage conditions