Real-time, online-editing web apps provide free and convenient services for collaboratively editing, sharing and storing files. The benefits of these web applications do not come for free: not only do service providers have full access to the users' files, but they also control access, transmission, and storage mechanisms for them. As a result, user data may be at risk of data mining, third-party interception, or even manipulation. To combat this, we propose a new system for helping to preserve the privacy of user data within collaborative environments. There are several distinct challenges in producing such a system, including developing an encryption mechanism that does not interfere with the back-end (and often proprietary) control mechanisms utilized by the service, and identifying transparent code hooks through which to obfuscate user data. Toward the first challenge, we develop a character-level encryption scheme that is more resilient to the types of attacks that plague classical substitution ciphers. For the second challenge, we design a browser extension that robustly demonstrates the feasibility of our approach, and show a concrete implementation for Google Chrome and the widely-used Google Docs platform. Our example tangibly demonstrates how several users with a shared key can collaboratively and transparently edit a Google Docs document without revealing the plaintext directly to Google.
M-Aegis @cite_17 aims to protect data from cloud providers by using a transparent window that sits atop an existing application and encrypts input data in transit. Our approach differs from M-Aegis in a number of ways.
@cite_17
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Users are increasingly storing, accessing, and exchanging data through public cloud services such as those provided by Google, Facebook, Apple, and Microsoft. Although users may want to have faith in cloud providers to provide good security protection, the confidentiality of any data in public clouds can be violated, and consequently, while providers may not be "doing evil," we can not and should not trust them with data confidentiality. To better protect the privacy of user data stored in the cloud, in this paper we propose a privacy-preserving system called Mimesis Aegis (M-Aegis) that is suitable for mobile platforms. M-Aegis is a new approach to user data privacy that not only provides isolation but also preserves the user experience through the creation of a conceptual layer called Layer 7.5 (L-7.5), which is interposed between the application (OSI Layer 7) and the user (Layer 8). This approach allows M-Aegis to implement true end-to-end encryption of user data with three goals in mind: 1) complete data and logic isolation from untrusted entities; 2) the preservation of original user experience with target apps; and 3) applicable to a large number of apps and resilient to app updates. In order to preserve the exact application workflow and look-and-feel, M-Aegis uses L-7.5 to put a transparent window on top of existing application GUIs to both intercept plaintext user input before transforming the input and feeding it to the underlying app, and to reverse-transform the output data from the app before displaying the plaintext data to the user. This technique allows M-Aegis to transparently integrate with most cloud services without hindering usability and without the need for reverse engineering. We implemented a prototype of M-Aegis on Android and show that it can support a number of popular cloud services, e.g. Gmail, Facebook Messenger, WhatsApp, etc. Our performance evaluation and user study show that users incur minimal overhead when adopting M-Aegis on Android: imperceptible encryption decryption latency and a low and adjustable false positive rate when searching over encrypted data. As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution. Data security in the cloud is a big concern that blocks the widespread use of the cloud for relational data management. First, to ensure data security, data confidentiality needs to be provided when data resides in storage as well as when data is dynamically accessed by queries. Prior works on query processing on encrypted data did not provide data confidentiality guarantees in both aspects. Tradeoff between secrecy and efficiency needs to be made when satisfying both aspects of data confidentiality while being suitable for practical use. Second, to support common relational data management functions, various types of queries such as exact queries, range queries, data updates, insertion and deletion should be supported. To address these issues, this paper proposes a comprehensive framework for secure and efficient query processing of relational data in the cloud. Our framework ensures data confidentiality using a salted IDA encoding scheme and column-access-via-proxy query processing primitives, and ensures query efficiency using matrix column accesses and a secure B+-tree index. In addition, our framework provides data availability and integrity. We establish the security of our proposal by a detailed security analysis and demonstrate the query efficiency of our proposal through an experimental evaluation. Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user's real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.