Cloud computing is now being utilized as a prospective alternative for catering storage service. Security issues of cloud storage are a potential deterrent in its widespread adoption. Privacy breach, malicious modification, and data loss are emerging cyber threats against cloud storage. Recently, a fog server based three-layer architecture has been presented for secure storage employing multiple clouds. The underlying techniques used are Hash-Solomon code and customized hash algorithm in order to attain the goal. However, it resulted in loss of smaller portion of data to cloud servers and failed to provide better modification detection and data recoverability. This paper proposes a novel fog-centric secure cloud storage scheme to protect data against unauthorized access, modification, and destruction. To prevent illegitimate access, the proposed scheme employs a new technique$Xor - Combination$to conceal data. Moreover,$Block - Management$outsources the outcomes of$Xor - Combination$to prevent malicious retrieval and to ensure better recoverability in case of data loss. Simultaneously, we propose a technique based on hash algorithm in order to facilitate modification detection with higher probability. We demonstrate robustness of the proposed scheme through security analysis. Experimental results validate performance supremacy of the proposed scheme compared to contemporary solutions in terms of data processing time.

In a period-timestamped, relational temporal database, each tuple is timestamped with a period. The timestamp records when the tuple is "alive" in some temporal dimension. Sequenced semantics is a special semantics for evaluating a query in a temporal database. The semantics stipulates that the query must, in effect, be evaluated simultaneously in each time instant using the tuples alive at that instant. Previous research has proposed changes to the query evaluation engine to support sequenced semantics. In this paper we show how to achieve sequenced semantics without modifying a query evaluation engine. Our technique has two pillars. First we use log-segmented timestamps to record a tuple’s lifetime. A log-segmented timestamp divides the time-line into segments of known length. Any temporal period can be represented by a small number of such segments. Second, by taking advantage of the properties of log-segmented timestamps, we translate a sequenced relational algebra query to a non-temporal relational algebra query, using the operations already present in an unmodified, non-temporal query evaluation engine. The primary contribution of this paper is how to implement sequenced semantics using log-segmented timestamped tuples in a generic DBMS, which, to the best of our knowledge, has not been previously shown.

Purpose With the rapid deployment of internet of things (IoT) technologies, it has been essential to address the security and privacy issues through maintaining transparency in data practices. The prior research focused on identifying people's privacy preferences in different contexts of IoT usage and their mental models of security threats. However, there is a dearth in existing literature to understand the mismatch between user's perceptions and the actual data practices of IoT devices. Such mismatches could lead users unknowingly sharing their private information, exposing themselves to unanticipated privacy risks. The paper aims to identify these mismatched privacy perceptions in this work. Design/methodology/approach The authors conducted a lab study with 42 participants, where they compared participants’ perceptions with the data practices stated in the privacy policy of 28 IoT devices from different categories, including health and exercise, entertainment, smart homes, toys and games and pets. Findings The authors identified the mismatched privacy perceptions of users in terms of data collection, sharing, protection and storage period. The findings revealed the mismatches between user's perceptions and the data practices of IoT devices for various types of information, including personal, contact, financial, heath, location, media, connected device, online social media and IoT device usage. Originality/value The findings from this study lead to the recommendations on designing simplified privacy notice by highlighting the unexpected data practices, which in turn, would contribute to the secure and privacy-preserving use of IoT devices.

User activity logs can be a valuable source of information in cloud forensic investigations; hence, ensuring the reliability and integrity of such logs is crucial. Most existing solutions for secure logging are designed for conventional systems rather than the complexity of a cloud environment. In this paper, we propose the Cloud Log Assuring Soundness and Secrecy (CLASS) process as an alternative scheme for the securing of logs in a cloud environment. In CLASS, logs are encrypted using the individual user's public key so that only the user is able to decrypt the content. In order to prevent unauthorized modification of the log, we generate proof of past log (PPL) using Rabin's fingerprint and Bloom filter. Such an approach reduces verification time significantly. Findings from our experiments deploying CLASS in OpenStack demonstrate the utility of CLASS in a real-world context.

With a rapid increase in the use of digital technologies, people in the Global South including Bangladesh are exposed to a wide-range of smartphone applications (termed as apps in this paper), which offer a variety of features and services. However, privacy leakage through apps has increasingly become a major concern in Bangladesh, where the app collecting users' sensitive information without their consent was reported in news media for privacy violation. Our study with 32 participants from varying age, literacy level, and profession in Dhaka, Bangladesh unveils the perceptions of people around data collection and sharing by the app reported in privacy leakage news. All of our participants were aware of information leakage through the app they use, where they possess varying perceptions around providing personal information, like a sense of benefit, necessity and contribution, indifference, fear, or (no) authority over data collection. Our analysis reveals the relation between users' privacy perceptions, local infrastructure, and social practices in Bangladesh, where we identify the situated challenges that interfere with people's understanding of privacy notice. Our results lead to a discussion on how people's privacy perceptions are influenced by rapid urbanization and the opportunities offered by digitization in Bangladesh. Based on our findings, we provide recommendations to develop situated and sustainable strategies to enhance privacy awareness and practices in the social setting of Bangladesh, and Global South.