Web users often follow hyperlinks hastily, expecting them to be correctly programmed. However, it is possible those links contain typos or other mistakes. By discovering active but erroneous hyperlinks, a malicious actor can spoof awebsite or service, impersonating the expected content and phishing private information. In typosquatting, misspellings of common domains are registered to exploit errors when users mistype a web address. Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these hijackable hyperlinks exist in large quantities with the potential to generate substantial traffic. Analyzing largescale crawls of the web using high-performance computing, we show the web currently contains active links to more than 572 000 dot-com domains that have never been registered, what we term phantom domains. Registering 51 of these, we see 88% of phantom domains exceeding the traffic of a control domain, with up to 10 times more visits. Our analysis shows that these links exist due to 17 common publisher error modes, with the phantom domains they point to free for anyone to purchase and exploit for under $20, representing a low barrier to entry for potential attackers.

The energy industry is undergoing significant transformations as it strives to achieve net-zero emissions and future-proof its infrastructure, where every participant in the power grid has the potential to both consume and produce energy resources. Federated learning -- which enables multiple participants to collaboratively train a model without aggregating the training data -- becomes a viable technology. However, the global model parameters that have to be shared for optimization are still susceptible to training data leakage. In this work, we propose confined gradient descent (CGD) that enhances the privacy of federated learning by eliminating the sharing of global model parameters. CGD exploits the fact that a gradient descent optimization can start with a set of discrete points and converges to another set in the neighborhood of the global minimum of the objective function. As such, each participant can independently initiate its own private global model~(referred to as the confined model ), and collaboratively learn it towards the optimum. The updates to their own models are worked out in a secure collaborative way during the training process.In such a manner, CGD retains the ability of learning from distributed data but greatly diminishes information sharing. Such a strategy also allows the proprietary confined models to adapt to the heterogeneity in federated learning, providing inherent benefits of fairness. We theoretically and empirically demonstrate that decentralized CGD øne provides a stronger differential privacy (DP) protection; \two is robust against the state-of-the-art poisoning privacy attacks; þree results in bounded fairness guarantee among participants; and \four provides high test accuracy (comparable with centralized learning) with a bounded convergence rate over four real-world datasets.

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world’s most marginalized children. However, while legislatures around the world have enacted regulations to protect children’s online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. We investigate 20195 mobile apps from the Google Play store that are designed particularly for children (Family apps) or include children in their target user groups (Normal apps). Using both static and dynamic analysis, we find that 4.47% of Family apps request location permissions, even though collecting location information from children is forbidden by the Play store, and 81.25% of Family apps use trackers (which are not allowed in children’s apps). Even major developers with 40+ kids apps on the Play store use ad trackers. Furthermore, we find that most permission request notifications are not well designed for children, and 19.25% apps have inconsistent content age ratings across the different protection authorities. Our findings suggest that, despite significant attention to children’s privacy, a large gap between regulatory provisions, app store policies, and actual development practices exist. Our research sheds light for government policymakers, app stores, and developers.

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that poses a significant threat to federated learning (FL). An adversary conducts data poisoning (i.e., performing adversarial manipulations on training examples) to extract membership information by exploiting the changes in loss resulting from data poisoning. The PMIA significantly exacerbates the traditional poisoning attack that is primarily focused on model corruption. However, there has been a lack of a comprehensive systematic study that thoroughly investigates this topic. In this work, we conduct a benchmark evaluation to assess the performance of PMIA against the Byzantine-robust FL setting that is specifically designed to mitigate poisoning attacks. We find that all existing coordinate-wise averaging mechanisms fail to defend against the PMIA, while the detect-then-drop strategy was proven to be effective in most cases, implying that the poison injection is memorized and the poisonous effect rarely dissipates. Inspired by this observation, we propose AgrEvader, a PMIA that maximizes the adversarial impact on the victim samples while circumventing the detection by Byzantine-robust mechanisms. AgrEvader significantly outperforms existing PMIAs. For instance, AgrEvader achieved a high attack accuracy of between 72.78% (on CIFAR-10) to 97.80% (on Texas100), which is an average accuracy increase of 13.89% compared to the strongest PMIA reported in the literature. We evaluated AgrEvader on five datasets across different domains, against a comprehensive list of threat models, which included black-box, gray-box and white-box models for targeted and non-targeted scenarios. AgrEvader demonstrated consistent high accuracy across all settings tested. The code is available at: https://github.com/PrivSecML/AgrEvader.

Businesses face a range of cyber risks, both external threats and internal vulnerabilities that continue to evolve over time. As cyber attacks continue to increase in complexity and sophistication, more organisations will experience them. For this reason, it is important that organisations seek timely consultancy from cyber professionals so that they can respond to and recover from cyber attacks as quickly as possible. However, huge surges in cyber attacks have long left cyber professionals short of what is required to cover the security needs. This problem is getting worse when an increasing number of people choose to work from home during the pandemic because this situation usually yields extra communication cost.

Cloud storage systems have seen a growing number of clients due to the fact that more and more businesses and governments are shifting away from in-house data servers and seeking cost-effective and ease-of-access solutions. However, the security of cloud storage is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB’s padding strategies ensure that the access pattern to the database is obfuscated all the time. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud.

Machine learning (ML) techniques are increasingly important in cybersecurity, as they can quickly analyse and identify different types of threats from millions of events. In spite of the increasing number of possible applications of ML, successful adoption of ML models in cybersecurity still highly relies on the explainability of those models that are used for making predictions. Explanations that support ML model outputs are crucial in cybersecurity-oriented ML applications because people need to get more information from the model than just binary output for analysis. The explainable models help ML developers solve the “trust” problem for a security application prediction in a faithful way: validating model behaviours, diagnosing misclassifications and sometimes automatically patching errors in the target models. Therefore, explainable ML for cybersecurity has become a necessary and important research branch. In this paper, we present the topic of explainable ML in cybersecurity through two general types of explanations: (1) ante hoc explanation, and (2) post hoc explanation, with their methodologies. We systematically review and categorise the state-of-the-art research, and provide comparative studies to help researchers find the optimal solutions to specific problems. We further list open issues in this field to facilitate future studies. This survey will benefit diverse groups of readers from both academia and industries, who want to effectively use ML to solve cybersecurity challenges.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which resulted in a disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.

With the ubiquitous presence of smart phones and the availability of easy-to-use applications, there is an increase in the number of online services. A growing number of people now search for information and interact online. They expect to see services available and accessible online. To meet citizens’ expectations, governments have also increased their online presence. However, information and services are not the only reasons people go online. People also build their social circle online, seeking support and empathy, looking for someone with whom they can talk and who can understand their situation and worries. Online communities (and social networks in general) have been shown to have the potential to provide social and emotional peer-support. Our work aimed at determining whether online communities could be deployed in the public administration domain, in particular to support people receiving welfare payments, with similar benefits. We hypothesized that an online community could provide such support to disadvantaged citizens. Toward testing this hypothesis, after a user requirements analysis and some preparatory work, we designed and developed an online community for a specific target group of welfare recipients, as a collaboration between CSIRO and the Australian Department of Human Services. The community was deployed for one year. In this paper, we briefly explain our aims and the work that went into preparing for the community. We introduce the portal and the support it offered. We then report our observations and findings about both the informational and emotional support participants received, through an analysis of the comments posted in the community, and whether this support was perceived as welcome and useful.

People always spend their time at a few important locations for various activities in groups during specific time slots, called place of activity (POA), e.g., resting at home among family members during night and working at office among colleagues during work time. Inferring such places is significant for not only the precise advertising on the commercial aspect but the identifying rallies or meetings among a group of people and tracking of the target individuals on the aspect of public security, e.g., locating and tracking suspected terrorists for anti-terrorist work. However, it is a challenge to map from big location data to places of activity due to the volume and complexity whilst giving rise to privacy concerns, e.g., personally important place mining. In the paper, a method for POA mining on big location data is proposed, named P-PAM, aiming at big data analytics and privacy concerns. We use a clustering algorithm to discover the place of activity, then adopt location entropy as reference of user diversity and take into account temporal variation, to infer place of activity. Further, robust privacy-preserving mechanisms under differential privacy are embedded into clustering results and location entropy evaluation that accesses to raw location data. We demonstrate the utility of our proposed approach with large-scale location datasets derived from geo-referenced social media. The experimental results suggest that the POA mining approach can successfully scale to big data scenarios whilst preserving individual user privacy.

With the explosive growth of location-aware devices and global adoption of social network applications, enormous volumes of spatiotemporal data are being produced. These can be perceived as gazetteers that record frequently visited locations, e.g. shopping malls and museums, and potentially more sensitive locations, e.g. an individual's home/work locations. Density-based clustering approaches are generally used for gazetteer discovery. However, existing clustering solutions are inefficient for big data scenarios and often disregard mobility features derived from trajectories data. Further, automated gazetteer discovery applications may cause privacy concerns. In this paper, we propose a sensitive gazetteer automated discovery approach based on Ω-cluster with robust privacy controls. The approach identifies sensitive gazetteers from massive trajectory data, with location entropy-based filtering used to reduce the number of uninteresting clusters whilst considering mobility features of trajectories. A parallelized solution is implemented to scale across the cloud using memory-oriented data processing solutions based upon Apache Spark. We embed this algorithm in a privacy-preserving mechanism and subsequently release sanitized gazetteers. Through extensive experiments using synthetic and real trajectory datasets from the location based social network (Twitter), we demonstrate the effectiveness and efficiency of our approach.

Provenance is information about the origin and creation of data. In data science and engineering, such information is useful and sometimes even critical. In spite of that, provenance for big data is under-explored due to the challenges from the `Vs' of big data. In data analytics, users need to query history, reproduce intermediate or final results, tune models, and adjust parameters in runtime for making data-driven decisions. In addition, users need to evaluate data and pipeline trustworthiness. Towards realising these functionalities for big data provenance, we propose a solution, called LogProv, which needs to renovate data pipelines or even some of big data software infrastructure to generate structured logs for pipeline events, and then stores data and logs separately. The data are explicitly linked to the logs, which implicitly record pipeline semantics. Semantic information can be retrieved from the logs easily since the logs are well defined and structured beforehand. We implemented LogProv in Apache Pig, and adopted ElasticSearch to provide query service. In this paper LogProv is evaluated in a Hadoop ecosystem hosted by a cloud and empirically case-studied. The results show that LogProv is efficient since the performance overhead is no more than 10%, the query can be responded within 1 second, the trustworthiness is marked clearly, and there is no impact on the data processing logic of original pipelines.

Unprecedented volumes of location-based information have been produced as a result of the widespread adoption of social network applications and GPS-enabled devices and sensors. Publication of such location data can provide valuable resources for researchers and government agencies in applications ranging from near real-time population-wide health monitoring to planning for future cities. However, such data hold personally identifying information, which gives rise to many privacy issues. There is thus a pressing need for ways to restrict this inherently identifying location-related information, however ideally we would like to preserve the utility of the data. Importantly, any such solution has to be scalable to large population-wide data scenarios. To tackle this, we introduce a novel differentially private hierarchical location sanitization (DPHLS) approach based on the concept “(α, r)-dataset” implemented through a Variable Order Mobility Markov Model (VO3M). We show how this system allows individual locations in personal trajectories to be protected using selection and frequency perturbation mechanisms using the “(α, r)-dataset”, leveraging past (published) location histories to obfuscate the user location in a flexible and controllable manner. The effectiveness and efficiency of the proposed solution is evaluated through the big data experiments that have been carried out using an OpenStack-based Cloud and Apache Sparkbased platform utilising large-scale social media trajectories. The experimental results suggest that the privacy publication algorithm can successfully scale to big data scenarios whilst retaining the utility of the datasets (trajectories) and preserving individual user privacy.

Cloud storage services have become ubiquitous. A large number of individuals and organizations are using them to store and share data, taking the benefits of mobility and affordability offered by these services. However, secure management of data in cloud storage services, more specifically supporting multi-party sharing in the context of a collaboration, is a challenging problem. The problem is further exacerbated if the data owner does not have any trust on the cloud storage providers and the data need regular updates from collaborating parties. A number of cryptographically enforced secure cloud storage solutions have been proposed to address this problem. One of the key issues with these solutions is the revocation of access to data for invalid users without moving the data (in the era of big data) and relying on the cloud service providers. In this paper, we introduce a cloud storage system that offers cryptographically enforced security. In contrast to other cryptographically protected cloud storage systems, our system supports a fine-grained access control mechanism and allows flexible revocations of invalid users without moving the data and relying on the cloud service providers. Our system employs an attribute-based encryption technique to support a complex access structure that allows a user to define human readable access policies to the data in the cloud storage. In addition, our system supports a flexible revocation scheme that can revoke invalid users directly by updating the revoked users’ list or indirectly by updating an epoch counter. The system administrator can choose one of these options flexibly depending on the needs. Our system also allows authorized users to update the encrypted data, and any users accessing such updated data in future can verify whether the data are modified by authorized users.

Gamification, the idea of inserting game dynamics into portals or social networks, has recently evolved as an approach to encourage active participation in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. We decided to introduce gamification in an online community that we designed and developed with the Australian Government's Department of Human Services to support welfare recipients transitioning from one payment to another. We first defined a formal model of gamification and a gamification design process. In instantiating our model to the online community, we realised that our context applied a number of constraints on the gamification elements that could be introduced. In this paper, we outline the design and implementation of a gamification model for online communities and its instantiation into our context, with its specific requirements. While we cannot comment on the success of gamification to drive user engagement in our context (for lack of the possibility of a controlled experiment), we found our implementation of badges-based gamification a helpful way to provide a useful abstraction on the life of the community, providing feedback enabling us to monitor and analyze the community. We thus show how feedback provided by such gamification data has a potential to be useful to community providers to better understand the community needs and addressing them appropriately to maintain a level of engagement in the community.

Content based retrieval systems have been developed for querying image data in which the users can pose queries based on visual properties such as color and texture. These systems, which have advanced the state of the art in image database systems, remarkably lack formal query languages. The traditional query languages are unable to capture the inherent fuzzy nature of the image data and content based querying. The fuzzy object query language (FOQL) presented in this paper addresses the need to support fuzzy values and fuzzy collections required for image databases. It can be used for defining schemas and high level concepts, and for querying image databases. It captures the inherent fuzzy nature of content based retrieval by keeping the query results fuzzy as against other query languages. The users can interactively refine their queries and high level concept definitions using recursive and named query definition constructs in FOQL. Being an extension of ODMG-OQL, FOQL can be easily mapped to ODMG-compliant visual query languages.

Multimedia database systems are essential for the effective and efficient use of large collections of image data. The aim of such systems is to enable retrieval of images based on their contents. As part of our research in this area, we are building a prototype content-based image retrieval system called CHITRA. This uses a four-level data model, and we have defined a fuzzy object query language (FOQL) for this system. This system enables retrieval based on high-level concepts, such as "retrieve images of mountains and sunset". A problem faced in this system is the processing of complex queries such as "retrieve all images that have a similar color histogram and a similar texture to the given example image". Such problems have attracted research attention in recent times. R. Fagin (1996) has given an algorithm for processing such queries and provided a probabilistic upper bound for the complexity of the algorithm (which has been implemented in IBM's Garlic project). In this paper, we provide a theoretical (probabilistic) analysis of the expected cost of this algorithm. We propose a new multi-step query processing algorithm and prove that it performs better than Fagin's algorithm in all cases. Our algorithm requires fewer database accesses. We have evaluated both algorithms against an image database of 1000 images on our CHITRA system. We have used both color histogram and Gabor texture features. Our analysis is presented and the reported experimental results validate our algorithm (which has a significant performance improvement).

No abstract available.