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.

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.

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.

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.

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.