Strategic signaling for utility control in audit games

As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to issue an alert when suspicious access is detected. Defenders can audit alerts to reduce potential damage. This interaction process between a defender and an attacker can be modeled as an audit game. In previous studies, it was found that sending real-time signals in the audit game to warn visitors can improve the benefits of the defender. However, the previous approaches usually assume perfect information of the attacker, or simply concentrate on the utility of the defender. In this paper, we introduce a brand-new zero-determinant (ZD) strategy to study the sequential audit game with online signaling, which empowers the defender to unilaterally control the utility of visitors when accessing sensitive data. In addition, an optimization scheme based on the ZD strategy is designed to effectively maximize the utility difference between the defender and the attacker. Extensive simulation results show that our proposed scheme enhances the security management and control capabilities of the defender to better handle different access requests and safeguard the system security in a cost-efficient manner

MMZDA: Enabling Social Welfare Maximization in Cross-Silo Federated Learning

—As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organizations in cross-silo FL as a public goods game for the first time and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To overcome this social dilemma, we employ the Multi-player Multi-action ZeroDeterminant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Since the MMZDstrategy can be adopted by all organizations, we further study the scenario where multiple organizations jointly adopt the MMZD strategy and form an MMZD Alliance (MMZDA). We prove theoretically that the MMZDA strategy strengthens the control of the maximum social welfare. Experimental results validate that the MMZD strategy is effective in maximizing the social welfare and the MMZDA can achieve a larger maximum value

De-anonymyzing scale-free social networks by using spectrum partitioning method

Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anonymization into node matching problem in graph, and the de-anonymization method can reduce the number of nodes to be matched at each time. In addition, we use spectrum partitioning method to divide the social graph into disjoint subgraphs, and it can effectively be applied to large-scale social networks and executed in parallel by using multiple processors. Through the analysis of the influence of power-law distribution on de-anonymization, we synthetically consider the structural and personal information of users which made the feature information of the user more practical

A survey on security analysis of Amazon echo devices

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is used for diverse tasks such as accessing online information, making phone calls, purchasing items, and controlling the smart home. Echo offers user-friendly voice interaction to automate everyday tasks making it a massive success. Though many people view Amazon Echo as a helpful assistant at home or office, few know its underlying security and privacy implications. In this paper, we present the findings of our research on Amazon Echo’s security and privacy concerns. The findings are divided into different categories by vulnerability or attacks. The proposed mitigation(s) to the vulnerabilities are also presented in the paper. We conclude that though numerous privacy concerns and security vulnerabilities associated with the device are mitigated, many vulnerabilities still need to be addressed

Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models

Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models

Clustering-Based Energy-Efficient Broadcast Tree in Wireless Networks

The characteristics of wireless networks present formidable challenges to the study of broadcasting problem. A crucial issue in wireless networks is the energy consumption, because of the nonlinear attenuation properties of radio signals. Another crucial issue is the trade-off between reaching more nodes in a single hop by using higher power versus reaching fewer nodes in that single hop by using lower power. Given a wireless network with a specified source node that broadcasts messages to all other nodes in the network, the minimum energy broadcast (MEB) problem is NP-hard. In this paper, we propose a hybrid approach CBEEB(clustering-based energy-efficient broadcast) for the MEB problem based on clustering. Theoretical analysis indicates the efficiency and effectiveness of CBEEB. Simulation results show that CBEEB has better performance compared with the existing heuristic approaches

A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model

As the proportion of distributed photovoltaic (DP) increases, improving the accuracy of regional distributed photovoltaic power calculation is crucial to making full use of PV and ensuring the safety of the power system. The calculation of regional power generation is the key to power prediction, performance evaluation, and fault diagnosis. Distributed photovoltaic plants (DPP) are characterized by scattered distribution and small installed capacity, lots of DPPs are not fully monitored, and their real-time output power is difficult to obtain. Therefore, to improve the observability of DPPs and increase the accuracy of calculation, a new method that combines with fuzzy c-means (FCM), Max-Relevance and Min-Redundancy (mRMR) and Extreme Learning Machine(ELM), which can calculate the regional DPP output power without meteorological data is proposed, and validated using actual operational data of regional DPPs in China. The calculations results show good robustness in different months. The innovation of this study is the combination of the benchmark DPP selection method FCM-mRMR and the power calculation method nELM, and the mean absolute error (MAPE) of the proposed method is 0.198 and the coefficient of determination (R2) is 0.996

A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model

As the proportion of distributed photovoltaic (DP) increases, improving the accuracy of regional distributed photovoltaic power calculation is crucial to making full use of PV and ensuring the safety of the power system. The calculation of regional power generation is the key to power prediction, performance evaluation, and fault diagnosis. Distributed photovoltaic plants (DPP) are characterized by scattered distribution and small installed capacity, lots of DPPs are not fully monitored, and their real-time output power is difficult to obtain. Therefore, to improve the observability of DPPs and increase the accuracy of calculation, a new method that combines with fuzzy c-means (FCM), Max-Relevance and Min-Redundancy (mRMR) and Extreme Learning Machine(ELM), which can calculate the regional DPP output power without meteorological data is proposed, and validated using actual operational data of regional DPPs in China. The calculations results show good robustness in different months. The innovation of this study is the combination of the benchmark DPP selection method FCM-mRMR and the power calculation method nELM, and the mean absolute error (MAPE) of the proposed method is 0.198 and the coefficient of determination (R2) is 0.996