Tempas

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the three years of IA access logs (2012 vs. 2015 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 (91% of requests) and IA 2015 (88% of requests) is greater than in IA 2019 (70% of requests). Robots account for 98% of requests in Arquivo.pt (2019). We found that the robots are almost entirely limited to “Dip” and “Skim” access patterns in IA 2012 and 2015, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.

To identify robots and humans and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012 and 2019, as well as Arquivo.pt's (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the two years of IA access logs (2012 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 is greater than in IA 2019 (21% more in requests and 18% more in sessions). Robots account for 98% of requests (97% of sessions) in Arquivo.pt (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.

Searchable encryption (SE) enables data users to securely search encrypted data stored in untrusted cloud servers. However, most SE schemes allow for leakages of access and search patterns to maximize efficiency and functionality. Recent attacks have shown that adversaries can recover query keywords with prior knowledge of the database by exploiting these leakages. Unfortunately, the existing schemes that protect access and search patterns result in frequent communications and high computational costs. Furthermore, complex calculation processes also raise challenges for verifying search results. To address these concerns, we first design an efficient conjunctive SE scheme with search and access pattern privacy using private set intersection. In the proposed scheme, we utilize random numbers to obfuscate the values of polynomials and randomly divide the results into two parts, which simplifies the search process, improves search efficiency, and eliminates the need for time-consuming ciphertext multiplication operations. We also extend this scheme to support search result verifiability. Specifically, by embedding a random number as the root of the return polynomial, we achieve verifiability of search results. Furthermore, we prove the security of both schemes employing the simulation-based method. Finally, we implement the schemes in a real database and thorough performance analyses demonstrate their efficiency.

We propose a web clustering method using social bookmarking data with dimension reduction regarding similarity. To realize this idea we construct the similarity matrix between web pages based on their cooccurrence frequency. Since the similarity matrix includes various kind of noise, we map the similarity matrix onto lower dimension feature space to reduce the noise. Especially we carry out dimension reduction regarding web pages' similarity. This approach uses generalized eigenvectors and is different from usual eigenvalue problems. Using artificially generated data, we explain that the feature space constructed with our proposed method emphasizes the essential relationship between web pages. And using real social bookmarking data, we describe our proposed method can make good clusters.

Text search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there is no scalable and principled solution to search such a collection as of a specified time. In this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search. We introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results. In order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance. These techniques can be formulated as optimization problems that can be solved to near-optimality. Finally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets. Results unequivocally show that our methods make it possible to build an efficient time machine scalable to large versioned text collections.

Document stores have become one of the key NoSQL storage solutions. They have been widely adopted in different domains due to their ability to store semi-structured data and expressive query capabilities. However, implementations differ in terms of concrete data storage and retrieval. Unfortunately, a standard framework for data and query optimization for document stores is nonexistent, and only implementation-specific design and query guidelines are used. Hence, the goal of this work is to aid automating the data design for document stores based on query costs instead of generic design rules. For this, we define a generic storage and query cost model based on disk access and memory allocation that allows estimating the impact of design decisions. Since all document stores carry out data operations in memory, we first estimate the memory usage by considering characteristics of the stored documents, their access patterns, and memory management algorithms. Then, using this estimation and metadata storage size, we introduce a cost model for random access queries. We validate our work on two well-known document store implementations: MongoDB and Couchbase. The results show that the memory usage estimates have the average precision of 91% and predicted costs are highly correlated to the actual execution times. During this work, we have managed to suggest several improvements to document storage systems. Thus, this cost model also contributes to identifying discordance between document store implementations and their theoretical expectations.

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Although processing speed, storage capacity and network bandwidth are steadily increasing, network latency remains a bottleneck for scientists accessing large remote data sets. This problem is most acute with n-dimensional data. Grid researchers have only recently begun to develop tools for efficient remote access to n-dimensional data sets. Within the context of the Granite Scientific Database system, we show that latency penalties can be dramatically reduced using explicit knowledge of a user's access pattern represented as an iterator. The iterator not only performs an n-dimensional iteration for the user, but also communicates the access pattern to Granite so that a prefetching cache can be constructed that is tuned to the user's access pattern. We experimentally evaluate a scenario for incorporating Granite's prefetching mechanism into the grid, demonstrating extraordinary performance gains. In light of these results, we describe planned additions to existing grid services to allow selection of datasets according to the user access pattern

Data partitioning is an effective way to reduce cost and improve query performance in large-scale Web data analytical applications. State-of-the-art partitioning approaches on range queries is lacking of considering the correlation of data in a certain access patterns, especially in some skewed patterns. This paper presents a correlation-aware partitioning model for skewed range queries. It formulates partitioning optimization issue on continuous correlated data as a geometrical step curve fitting problem. Then, we prove that the optimal partitioning should split data on range query boundaries. On this basis, Range Boundary Based DP Partitioning is designed to induce the optimal partition and significantly reduce the computation cost compared to the baseline dynamic programming algorithm. Local is better than global. For efficiency, Bottom-up Merging Partitioning is proposed further to improve partitioning by bottom-up merging instead of searching. To evaluate the proposed approaches, sets of experiments are conducted under skewed range query workloads on skewed and uniform datasets, and show they do optimize the efficiency of data partitioning by hundreds of times.

Outsourcing encrypted data to the cloud demands efficient and confidentiality-preserving search techniques, yet existing Searchable Symmetric Encryption (SSE) schemes suffer from severe access and search pattern leakage, and the problem is more intractable in multi-data-owner scenarios where cross-dataset privacy-preserving search remains unsolved. This paper proposes PSSE-MDO (Privacy-Preserving SSE with Pattern Obfuscation for Multiple Data Owners), the first SSE scheme that achieves robust access and search pattern hiding for multi-data-owner settings with constant client-side overhead. Our core innovation is a cryptographic protocol enabling users to issue a single constant-sized search token (trapdoor) for searching across all data owners' encrypted indexes. The scheme integrates Inner-Product Predicate Encryption (IPE) and Somewhat Homomorphic Encryption (SHE) to probabilistically obfuscate search results for each query. A key experimental result shows that the client-side cost approximately 120 ms for search token generation—remains constant regardless of the number of data owners, in contrast to existing pattern-hiding schemes with linearly scaling costs. This validates PSSE-MDO's practicality for real-world large-scale collaborative cloud systems, marking a significant step toward practical privacy-preserving search in federated cloud environments.

SummarySecuring user identity from data breach in a web server is one of the major concerns for the users of the web applications. Similarly, protecting user access pattern from unauthorized access should be taken seriously, because the potential threats such attacks may pose, are huge. However, these security measures should not be adopted at the expense of user experience and convenience. Nevertheless, any extra overhead in the form of security measures introduced in a distributed system results in significant performance declination. The target of a secured framework for a distributed system like web application should be a reasonable trade‐off between security and user experience. Thus, in this work, we present a framework that ensures security for the user identity along with keeping the online activities of the users anonymous while ensuring scalability of the system. Our framework is designed in a scalable form that can work with other distributed architectures that provide security to user data and identities. To ensure all these measures, our proposal includes the implementation of Forward Secrecy using Diffie‐Hellman Key exchange protocol where the server cannot remember a user's history after a session ends. In addition, we present our own mechanism to hide logical data sharing strategies to protect users against selective DoS attacks. Moreover, we implemented a modified version of bloom filter to safeguard user access pattern in a compromised server. Our proposed implementation of bloom filter also ensures that the scalability of distributed system is preserved even with little infrequent overhead in the server because of security measures proposed in this work. Finally, we implemented different modules of our framework using both Web Socket and Long Polling transport protocols and recorded the time required to perform various tasks. Web socket protocol took less time to perform each task than the long polling protocol, which is convincing enough to suggest that web socket performs better than long polling in the given scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

Cloud computing has greatly facilitated large-scale data outsourcing due to its cost efficiency, scalability and many other advantages. Subsequent privacy risks force data owners to encrypt sensitive data, hence making the outsourced data no longer searchable. Searchable Symmetric Encryption (SSE) is an advanced cryptographic primitive addressing the above issue, which maintains efficient keyword search over encrypted data without disclosing much information to the storage provider. Existing SSE schemes implicitly assume that original user data is centralized, so that a searchable index can be built at once. Nevertheless, especially in cloud computing applications, user-side data centralization is not reasonable, e.g. an enterprise distributes its data in several data centers. In this paper, we propose the notion of Multi-Data-Source SSE (MDS-SSE), which allows each data source to build a local index individually and enables the storage provider to merge all local indexes into a global index afterwards. We propose a novel MDS-SSE scheme, in which an adversary only learns the number of data sources, the number of entire data files, the access pattern and the search pattern, but not any other distribution information such as how data files or search results are distributed over data sources. We offer rigorous security proof of our scheme, and report experimental results to demonstrate the efficiency of our scheme.

Efficient searchable symmetric encryption for storing multiple source dynamic social data on cloud

With the advancement of technology, the digital archive of news history is becoming increasingly difficult to navigate due to the prevalence of overlapping and contextually accurate information on the Internet. By taking into account both the user's request and the item's content circulation over time, narrative building aims to enhance information retrieval. By using the constructive time aspects of both inquiries and articles to maximize the search output, this research introduces a new method for developing query modeling in news archives via temporal unsupervised learning. Using the approach to analyse news data that is already timestamped, the method can dynamically identify temporal intent and sensitive time entities and relate documents to the temporal extent of the query. To achieve this, we propose a model that utilizes temporal profiles in conjunction with expansion query technology and temporal re-ranking, enabling the retrieval of relevant documents from specific periods. When compared to baseline models, benchmark news datasets demonstrated superior retrieval performance, with the most notable improvements confirmed for queries with a stronger temporal dependency. Ideal for accessing archives, researching history, and retrieving event-centric material, this technique also consistently captures the drift of a subject with the appearance of details throughout time. This study provides a practical way to improve the temporal search experience in digital journalism archives and highlights the significance of temporal modeling for dynamically capturing global news developments.

Affordable cloud computing technologies allow users to efficiently store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. This in turn improves the quality of healthcare services, and lower health care cost. However, serious security and privacy concerns emerge because people upload their personal information and PHRs to the public cloud. Data encryption provides privacy protection of medical information but it is challenging to utilize encrypted data. In this paper, we present a privacy-preserving disease treatment, complication prediction scheme (PDTCPS), which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. $PDTCPS$ uses a tree-based structure to boost search efficiency, a wildcard approach to support fuzzy keyword search, and a Bloom-filter to improve search accuracy and storage efficiency. In addition, our design also allows health care providers and the public cloud to collectively generate aggregated training models for disease diagnosis, personalized treatments and complications prediction. Moreover, our design provides query unlinkability and hides both search & access patterns. Finally, our evaluation results using two UCI datasets show that our scheme is more efficient and accurate than two existing schemes.