Arbitrary Function Evaluation with Fully Homomorphic Encryption Using Table Lookup

早稲田大学博士(工学)早大学位記番号:新9500doctoral thesi

Look-Up Table based FHE System for Privacy Preserving Anomaly Detection in Smart Grids

In advanced metering infrastructure (AMI), the customers\u27 power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter\u27s data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required by the anomaly detection framework are stored to enable arbitrary arithmetic calculations over FHE. Furthermore, we adopt a private information retrieval (PIR) approach with FHE to enable approximate search with LUTs, which reduces the execution time of the attack detection service while protecting private information. Besides, we show that by adjusting the significant digits of inputs and outputs in our LUT, we can control the detection accuracy and execution time of the attack detection, even while using FHE. Our experiments confirmed that our proposed method is able to detect the injection of false power consumption in the range of 11-17 secs of execution time, depending on detection accuracy

Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers\u27 fine-grained power consumption data; however, analyzing customers\u27 private data violates the customers\u27 privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme

Overexpression of N-Myc Downstream-Regulated Gene 2 (NDRG2) Regulates the Proliferation and Invasion of Bladder Cancer Cells In Vitro and In Vivo

N-Myc downstream-regulated gene 2 (NDRG2) is a candidate tumor suppressor gene, which plays an important role in controlling tumor growth. The aim of this study was to investigate the expression of NDRG2 gene in bladder cancer (BC) tissues and several bladder cancer cell lines, and to seek its clinical and pathological significance. Ninety-seven bladder carcinoma and 15 normal bladder tissue sections were analyzed retrospectively with immunohistochemistry. The human bladder cancer cell line T24 was infected with LEN-NDRG2 or LEN-LacZ. The effects of NDRG2 overexpression on T24 cells and T24 nude mouse xenografts were measured via cell growth curves, tumor growth curves, flow cytometric analysis, western blot and Transwell assay. NDRG2 was highly expressed in normal bladder tissue, but absent or rarely expressed in cacinomatous tissues (χ2=8.761, p < 0.01). The NDRG2 level was negatively correlated with tumor grade and pathologic stage(r=-0.248, p < 0.05), as well as increased c-myc level (r=-0.454, p< 0.001). The expression of NDRG2 was low in the three BC cell lines. T24 cells infected with LEN-NDRG2 showed inhibition of proliferation both in vitro and in vivo, and NDRG2 overexpression can inhibit tumor growth and invasion in vitro

MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Robust multi-agent reinforcement learning (MARL) necessitates resilience to uncertain or worst-case actions by unknown allies. Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios. Attempts to simplify this complexity often yield overly pessimistic policies, inadequate robustness across scenarios and high computational demands. Unlike these approaches, humans naturally learn adaptive and resilient behaviors without the necessity of preparing for every conceivable worst-case scenario. Motivated by this, we propose MIR2, which trains policy in routine scenarios and minimize Mutual Information as Robust Regularization. Theoretically, we frame robustness as an inference problem and prove that minimizing mutual information between histories and actions implicitly maximizes a lower bound on robustness under certain assumptions. Further analysis reveals that our proposed approach prevents agents from overreacting to others through an information bottleneck and aligns the policy with a robust action prior. Empirically, our MIR2 displays even greater resilience against worst-case adversaries than max-min optimization in StarCraft II, Multi-agent Mujoco and rendezvous. Our superiority is consistent when deployed in challenging real-world robot swarm control scenario. See code and demo videos in Supplementary Materials

Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game

In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions due to malfunction or adversarial attack. To address the uncertainty that any agent can be adversarial, we propose a Bayesian Adversarial Robust Dec-POMDP (BARDec-POMDP) framework, which views Byzantine adversaries as nature-dictated types, represented by a separate transition. This allows agents to learn policies grounded on their posterior beliefs about the type of other agents, fostering collaboration with identified allies and minimizing vulnerability to adversarial manipulation. We define the optimal solution to the BARDec-POMDP as an ex post robust Bayesian Markov perfect equilibrium, which we proof to exist and weakly dominates the equilibrium of previous robust MARL approaches. To realize this equilibrium, we put forward a two-timescale actor-critic algorithm with almost sure convergence under specific conditions. Experimentation on matrix games, level-based foraging and StarCraft II indicate that, even under worst-case perturbations, our method successfully acquires intricate micromanagement skills and adaptively aligns with allies, demonstrating resilience against non-oblivious adversaries, random allies, observation-based attacks, and transfer-based attacks

Inhibition of Female and Male Human Detrusor Smooth Muscle Contraction by the Rac Inhibitors EHT1864 and NSC23766

Introduction: Lower urinary tract symptoms (LUTS) due to overactive bladder (OAB) are caused by spontaneous detrusor contractions. Medical treatment with muscarinic receptor antagonists or β3-adrenoceptor agonists aims to inhibit detrusor contractions, but overall results are unsatisfactory. Consequently, improved understanding of bladder smooth muscle contraction and identification of novel compounds for its inhibition are needed to develop alternative options. A role of the GTPase Rac1 for smooth muscle contraction has been reported from the prostate, but is unknown in the human detrusor. Here, we examined effects of the Rac inhibitors NSC23766, which may also antagonize muscarinic receptors, and EHT1864 on contraction of human detrusor tissues. Methods: Female and male human detrusor tissues were obtained from radical cystectomy. Effects of NSC23766 (100 µM) and EHT1864 (100 µM) on detrusor contractions were studied in an organ bath. Results: Electric field stimulation induced frequency-dependent contractions of detrusor tissues, which were inhibited by NSC23766 and EHT1864. Carbachol induced concentration-dependent contractions. Concentration response curves for carbachol were shifted to the right by NSC23766, reflected by increased EC50 values, but unchanged Emax values. EHT1864 reduced carbachol-induced contractions, resulting in reduced Emax values for carbachol. The thromboxane analog U46619 induced concentration-dependent contractions, which remained unchanged by NSC23766, but were reduced by EHT1864. Conclusions: NSC23766 and EHT1864 inhibit female and male human detrusor contractions. NSC23766, but not EHT1864 competitively antagonizes muscarinic receptors. In addition to neurogenic and cholinergic contractions, EHT1864 inhibits thromboxane A2-induced detrusor contractions. The latter may be promising, as the origin of spontaneous detrusor contractions in OAB is noncholinergic. In vivo, both compounds may improve OAB-related LUTS