In today’s world where generative artificial intelligence has almost become an integral part of the coding, new challenges must be faced. Therefore, evaluating software bugs in both human written and AI generated code can be useful for the developers. A comparative analysis of these two coding practices is not only helpful for the decisions taken by the developers, it will also assist to give a direction on how to improve the quality of AI driven code. Currently, researchers have leveraged the role of software metrics to compare human written code and AI generated code as these metrics have long been utilized for software bug and vulnerability prediction by the researchers. They also analyzed the secure coding practices in terms of the number of bugs found in both AI and human written code. Our study is an extension of the current works as this study focuses on a set of metrics and a set of bugs as identified by some static analyzer tools. Investigating these two coding practices from different angles can help to reveal unknown relationships and factors that further can be analyzed to improve code quality of recent AI tools. Therefore, the main objective of our work is to identify the relationships between software metrics and bugs in AI generated code and human written code to compare and contrast the coding profiles of the two approaches. This will offer developers critical knowledge to enhance their strategies in mitigating potential bug risks across different coding methodologies. We have utilized top-rated Java solutions to 90 LeetCode problems, generated corresponding AI Java solutions to them, and utilized various static analysis tools to collect metrics and bugs. In this study, we found that two software metrics CountLineCodeDecl and CountLineCodeExe are positively correlated with the bug DLS_DEAD_LOCAL_STORE and the metric AvgCyclomatic is related to the bug AvoidLiteralsInIfCondition in both human written and AI generated code. These findings provide developers with critical insights into potential bug risks, enabling more effective mitigation strategies across different coding methodologies.

Preventing software vulnerabilities while writing code is one of the most effective ways for avoiding cyber attacks on any developed system. Although developers follow some standard guiding principles for ensuring secure code, the code can still have security bottlenecks and be compromised by an attacker. Therefore, assessing software security while developing code can help developers in writing vulnerability free code. Researchers have already focused on metrics-based and text mining based software vulnerability prediction models. The metrics based models showed higher precision in predicting vulnerabilities although the recall rate is low. In addition, current research did not investigate the impact of individual software metric on the occurrences of vulnerabilities. The main objective of this paper is to track the changes in every software metric after the developer fixes a particular vulnerability. The results of our research will potentially motivate further research on building more accurate vulnerability prediction models based on the appropriate software metrics. In particular, we have compared a total of 250 files from Apache Tomcat and Apache CXF. These files were extracted from the Apache database and were chosen because Apache released these files as vulnerable in their publicly available security advisories. Using a static analysis tool, metrics of the targeted vulnerable files and relevant fixed files (files where vulnerable code is removed by the developers) were extracted and compared. We show that eight of the 40 metrics have an average increase of 2% from vulnerable to fixed files. These metrics include CountDeclClass, CountDeclClassMethod, CountDeclClassVariable, CountDeclInstanceVariable, CountDeclMethodDefault, CountLineCode, MaxCyclomaticStrict, MaxNesting. This study will help developers to assess software security through utilizing software metrics in secure coding practices.

This Innovative Practice Full Paper presents several educational simulation designs to transform learning in the earth and environmental sciences. In recent years, K-12 education has seen a widespread pedagogical shift from traditional textbook-based learning to a student-centered interactive learning environment. Innovative teaching technologies including games and simulations are exploited to make learning fun and engaging for students at various grades. As technology continues to advances, stakeholders such as teachers, parents, and educational policymakers are motivated to know the most effective technology platform to engage students in active learning. In this paper, we discuss some useful simulation techniques from the perspectives of users' experience, system deployment and developers' point of view. In addition, we also present the design and implementation of some Earth and Environmental Science simulations that are developed using NetLogo and JavaScript. Some student assessment results are also provided to compare the learning effectiveness between the traditional textbooks and computer simulation-based lessons. Our results show that computer simulation can help enhance the student's content retention and graph interpretation skills.

Big Data management and analytics has revolutionized the way how organizations collect, store, process and retrieve, huge volumes of data. In order to fully leverage the potential of big data, it is often that organizations need to collaborate and analyze their combined data, and thus, improving the accuracy of results. However, due to government regulations and internal privacy policies, organizations cannot freely share their data with one another. Existing secure multiparty computation techniques along this direction are very expensive. In this paper, we develop a protocol that facilitates multiple users to outsource their encrypted databases as well as the frequent itemset mining task to a cloud environment in a collaborative and privacy-preserving manner. Our solution is built using the well-known apriori algorithm in order to boost the performance of frequent itemset mining in the cloud. Our comprehensive analysis has demonstrated that the proposed solution preserves the confidentiality of participating users. Additionally, our solution ensures that the entire frequent itemset mining task is performed on the cloud-side, thereby fully utilizing the cloud computing services to handle big data needs and incurring negligible cost on the end-users.

With continuing developments in miniaturization and battery design, wireless sensor networks (WSNs) are poised to become common technology in our daily lives. Low cost and flexibility of deployment make WSNs well suited for a wide variety of military, environmental, healthcare, and commercial applications. Some WSN applications, such as monitoring patients in hospitals or weapons targeting in battlefront require end-to-end data confidentiality. However, since WSNs are made up of many resource limited sensor nodes, they are typically unable to sustain the high volumes of data transmissions. Using in-network data aggregation, sensor data from multiple nodes can be combined before being forwarded to neighboring nodes; and thus, energy consumption can be reduced significantly. But in situations where sensor nodes privacy is non-negotiable, data aggregation cannot be implemented at the cost of security. Therefore, there is a strong need for secure data aggregation (SDA) protocols designed to fit the unique properties and considerable constraints of WSNs. Existing end-to-end solutions are either insecure or impractical. In this paper, we propose a novel solution for the secure aggregation of data in WSNs based on probabilistic homomorphic encryption. By combining with a unique encoding function, our solution guarantees the privacy of sensor data, while also greatly reducing communication costs.

The Jaccard Coefficient, as an information similarity measure, has wide variety of applications, such as cluster analysis and image segmentation. Due to the concerns of personal privacy, the Jaccard Coefficient cannot be computed directly between two independently owned datasets. The problem, secure computation of the Jaccard Coefficient for multisets (SJCM), considers the situation where two parties want to securely compute the random shares of the Jaccard Coefficient between their multisets. During the process, the content of each party's multiset is not disclosed to the other party and also the value of Jaccard Coefficient should be hidden from both parties. Secure computation of multiset intersection cardinality is an important sub-problem of SJCM. Existing methods when applied to solve such a problem can lead to either insecure or inefficient solutions. Our work addresses this gap. We first present a basic SJCM protocol constructed using the existing secure dot product method as a sub-routine. Then, as a major contribution, we propose an approximated version of our basic protocol to improve efficiency without compromising accuracy much. We provide various experimental results to show that the proposed protocols are significantly more efficient than the existing techniques when the domain size is small using both simulated and real datasets.

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy-preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user's input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings.

Data is one of the most valuable assets for organization. It can facilitate users or organizations to meet their diverse goals, ranging from scientific advances to business intelligence. Due to the tremendous growth of data, the notion of big data has certainly gained momentum in recent years. Clo

For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.

With the growing popularity of data and service outsourcing, where the data resides on remote servers in encrypted form, there remain open questions about what kind of query operations can be performed on the encrypted data. In this paper, we focus on one such important query operation, namely range query. One of the basic security primitive that can be used to evaluate range queries is secure comparison of encrypted integers. However, the existing secure comparison protocols strongly rely on the encrypted bit-wise representations rather than on pure encrypted integers. Therefore, in this paper, we first propose an efficient method for converting an encrypted integer z into encryptions of the individual bits of z. We then utilize the proposed security primitive to construct a new protocol for secure evaluation of range queries in the cloud computing environment. Furthermore, we empirically show the efficiency gains of using our security primitive over existing method under the range query application.

Many secure data analysis tasks, such as secure clustering and classification, require efficient mechanisms to convert the intermediate encrypted integers into the corresponding encryptions of bits. The existing bit-decomposition algorithms either do not offer sufficient security or are computationally inefficient. In order to provide better security as well as to improve efficiency, we propose a novel probabilistic-based secure bit-decomposition protocol for values encrypted using public key additive homomorphic encryption schemes. The proposed protocol guarantees security as per the semi-honest security definition of secure multi-party computation (MPC) and is also very efficient compared to the existing method. Our protocol always returns the correct result, however, it is probabilistic in the sense that the correct result can be generated in the first run itself with very high probability. The computation time of the proposed protocol grows linearly with the input domain size in bits. We theoretically analyze the complexity of the proposed protocol with the existing method in detail.

Wireless sensor networks (WSNs) have wide range of applications in military, health-monitoring, smart-home applications, and in other commercial environments. The computation of data aggregation functions like MIN/MAX is one of the commonly used tasks in many such WSN applications. However, due to privacy issues in some of these applications, the individual sensor readings should be kept secret from others. That is, the base station should be the only entity who should receive the output of MIN/MAX function and the individual sensor readings should not be revealed either to other sensor nodes or to the root node for confidentiality reasons. Existing Secure Data Aggregation (SDA) techniques for computing MIN/MAX are based on either order preserving or privacy homomorphic encryption schemes which are either inefficient or insecure. Along this direction, this paper proposes two novel solutions for securely computing MIN/MAX functions in WSNs using probabilistic encryption scheme. The first solution works for WSNs with no duplicate sensor readings whereas the second solution acts as a generic method and works even for duplicate readings but is less efficient compared to the first method. However, the second solution is much more secure compared to the existing protocols. The security of the proposed protocols is justified based on the well known quadratic residuosity assumption. We empirically analyze the efficiency of our schemes and demonstrate the advantages of the proposed protocols over existing approaches.

The emerging growth of online social networks have opened new doors for various business applications such as promoting a new product across its customers. Besides this, friend recommendation is an important tool for recommending potential candidates as friends to users in order to enhance the development of the entire network structure. Existing friend recommendation methods utilize social network structure and/or user profile information. However, these techniques can no longer be applicable if the privacy of users is taken into consideration. In this paper, we propose a two-phase private friend recommendation protocol for recommending friends to a given target user based on the network structure as well as utilizing the real message interaction between users. Our protocol computes the recommendation scores of all users who are within a radius of h from the target user in a privacy preserving manner. In addition, we show the practical applicability of our approach through empirical analysis.