Biblio

Privacy-preserving range queries allow encrypting data while still enabling queries on ciphertexts if their corresponding plaintexts fall within a requested range. This provides a data owner the possibility to outsource data collections to a cloud service provider without sacrificing privacy nor losing functionality of filtering this data. However, existing methods for range queries either leak additional information (like the ordering of the complete data set) or slow down the search process tremendously by requiring to query each ciphertext in the data collection. We present a novel scheme that only leaks the access pattern while supporting amortized poly-logarithmic search time. Our construction is based on the novel idea of enabling the cloud service provider to compare requested range queries. By doing so, the cloud service provider can use the access pattern to speed-up search time for range queries in the future. On the one hand, values that have fallen within a queried range, are stored in an interactively built index for future requests. On the other hand, values that have not been queried do not leak any information to the cloud service provider and stay perfectly secure. In order to show its practicability we have implemented our scheme and give a detailed runtime evaluation.

The Google Identity Platform is a system that allows a user to sign in to applications and other services by using a Google account. Google Sign-In is one such method for providing one’s identity to the Google Identity Platform. Google Sign-In is available for Android applications and iOS applications, as well as for websites and other devices. Users of Google Sign-In find that it integrates well with the Android platform, but iOS users (iPhone, iPad, etc.) do not have the same experience. The user experience when logging in to a Google account on an iOS application can not only be more tedious than the Android experience, but it also conditions users to engage in behaviors that put the information in their Google accounts at risk.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency Õ(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency Õ(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

Encrypting Internet communications has been the subject of renewed focus in recent years. In order to add end-to-end encryption to legacy applications without losing the convenience of full-text search, ShadowCrypt and Mimesis Aegis use a new cryptographic technique called "efficiently deployable efficiently searchable encryption" (EDESE) that allows a standard full-text search system to perform searches on encrypted data. Compared to other recent techniques for searching on encrypted data, EDESE schemes leak a great deal of statistical information about the encrypted messages and the keywords they contain. Until now, the practical impact of this leakage has been difficult to quantify. In this paper, we show that the adversary's task of matching plaintext keywords to the opaque cryptographic identifiers used in EDESE can be reduced to the well-known combinatorial optimization problem of weighted graph matching (WGM). Using real email and chat data, we show how off-the-shelf WGM solvers can be used to accurately and efficiently recover hundreds of the most common plaintext keywords from a set of EDESE-encrypted messages. We show how to recover the tags from Bloom filters so that the WGM solver can be used with the set of encrypted messages that utilizes a Bloom filter to encode its search tags. We also show that the attack can be mitigated by carefully configuring Bloom filter parameters.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

We give attacks on Feistel-based format-preserving encryption (FPE) schemes that succeed in message recovery (not merely distinguishing scheme outputs from random) when the message space is small. For \$4\$-bit messages, the attacks fully recover the target message using \$2textasciicircum1 examples for the FF3 NIST standard and \$2textasciicircum5 examples for the FF1 NIST standard. The examples include only three messages per tweak, which is what makes the attacks non-trivial even though the total number of examples exceeds the size of the domain. The attacks are rigorously analyzed in a new definitional framework of message-recovery security. The attacks are easily put out of reach by increasing the number of Feistel rounds in the standards.

iOS is well-known operating system which is strong in security. However, many attacking methods of iOS have recently been published which are called "Masque Attack", "Null Dereference" and "Italy Hacking Team's RCS". Therefore, security and safety is not suitable word to iOS. In addition, many security researchers have a problem to analyze iOS because the iOS is difficult to debug because of closed source. So, we propose a new security testing method for iOS. At first, we perform to fuzz iOS's web browser called MobileSafari. The MobileSafari is possible to express HTML, PDF and mp4, etc. We perform test abnormal HTML and PDF using our fuzzing method. We hope that our research can be helpful to iOS's security and safety.

Mobile applications - or apps - are one of the main reasons for the unprecedented success smart phones and tablets have experienced over the last decade. Apps are the main interfaces that users deal with when engaging in online banking, checking travel itineraries, or browsing their social network profiles while on the go. Previous research has studied various aspects of mobile application security including data leakage and privilege escalation through confused deputy attacks. However, the vast majority of mobile application research targets Google's Android platform. Few research papers analyze iOS applications and those that focus on the Apple environment perform their analysis on comparatively small datasets (i.e., thousands in iOS vs. hundreds of thousands in Android). As these smaller datasets call into question how representative the gained results are, we propose, implement, and evaluate CRiOS, a fully-automated system that allows us to amass comprehensive datasets of iOS applications which we subject to large-scale analysis. To advance academic research into the iOS platform and its apps, we plan on releasing CRiOS as an open source project. We also use CRiOS to aggregate a dataset of 43,404 iOS applications. Equipped with this dataset we analyze the collected apps to identify third-party libraries that are common among many applications. We also investigate the network communication endpoints referenced by the applications with respect to the endpoints' correct use of TLS/SSL certificates. In summary, we find that the average iOS application consists of 60.2% library classes and only 39.8% developer-authored content. Furthermore, we find that 9.32% of referenced network connection endpoints either entirely omit to cryptographically protect network communications or present untrustworthy SSL certificates.

This paper reviews the challenges faced when securing data on mobile devices. After a discussion of the state-of-the-art of secure storage for iOS and Android, the paper introduces an attack which demonstrates how Full Disk Encryption (FDE) on Android can be ineffective in practice.

The Google Identity Platform is a system that allows a user to sign in to applications and other services by using a Google account. Google Sign-In is one such method for providing one’s identity to the Google Identity Platform. Google Sign-In is available for Android applications and iOS applications, as well as for websites and other devices. Users of Google Sign-In find that it integrates well with the Android platform, but iOS users (iPhone, iPad, etc.) do not have the same experience. The user experience when logging in to a Google account on an iOS application can not only be more tedious than the Android experience, but it also conditions users to engage in behaviors that put the information in their Google accounts at risk.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency Õ(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency Õ(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

iOS is well-known operating system which is strong in security. However, many attacking methods of iOS have recently been published which are called "Masque Attack", "Null Dereference" and "Italy Hacking Team's RCS". Therefore, security and safety is not suitable word to iOS. In addition, many security researchers have a problem to analyze iOS because the iOS is difficult to debug because of closed source. So, we propose a new security testing method for iOS. At first, we perform to fuzz iOS's web browser called MobileSafari. The MobileSafari is possible to express HTML, PDF and mp4, etc. We perform test abnormal HTML and PDF using our fuzzing method. We hope that our research can be helpful to iOS's security and safety.

This paper reviews the challenges faced when securing data on mobile devices. After a discussion of the state-of-the-art of secure storage for iOS and Android, the paper introduces an attack which demonstrates how Full Disk Encryption (FDE) on Android can be ineffective in practice.