DF-ORAM: A Practical Dummy Free Oblivious RAM to Protect Outsourced Data Access Pattern

2016 ◽  
pp. 415-432 ◽  
Author(s):  
Qiumao Ma ◽  
Wensheng Zhang ◽  
Jinsheng Zhang
Keyword(s):  
Data Access ◽  
Access Pattern ◽  
Oblivious Ram ◽  
Outsourced Data

scholarly journals Flat ORAM: A Simplified Write-Only Oblivious RAM Construction for Secure Processors

Cryptography ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 10 ◽  
Author(s):  
Syed Haider ◽  
Marten van Dijk
Keyword(s):  
Energy Savings ◽  
Performance Gain ◽  
Access Pattern ◽  
Performance Bottlenecks ◽  
Oblivious Ram ◽  
Privacy Leakage ◽  
Cryptographic Primitive ◽  
Secure Processors ◽  
Performance Penalty ◽  
Access Patterns

Oblivious RAM (ORAM) is a cryptographic primitive which obfuscates the access patterns to a storage, thereby preventing privacy leakage. So far in the current literature, only ‘fully functional’ ORAMs are widely studied which can protect, at a cost of considerable performance penalty, against the strong adversaries who can monitor all read and write operations. However, recent research has shown that information can still be leaked even if only the write access pattern (not reads) is visible to the adversary. For such weaker adversaries, a fully functional ORAM turns out to be an overkill, causing unnecessary overheads. Instead, a simple ‘write-only’ ORAM is sufficient, and, more interestingly, is preferred as it can offer far better performance and energy efficiency than a fully functional ORAM. In this work, we present Flat ORAM: an efficient write-only ORAM scheme which outperforms the closest existing write-only ORAM called HIVE. HIVE suffers from performance bottlenecks while managing the memory occupancy information vital for correctness of the protocol. Flat ORAM introduces a simple idea of Occupancy Map (OccMap) to efficiently manage the memory occupancy information resulting in far better performance. Our simulation results show that, compared to HIVE, Flat ORAM offers 50 % performance gain on average and up to 80 % energy savings.

scholarly journals A replacement strategy for a distributed caching system based on the spatiotemporal access pattern of geospatial data

2014 ◽  
Vol XL-4 ◽  
pp. 133-137 ◽  
Author(s):  
R. Li ◽  
X. Wang ◽  
X. Shi
Keyword(s):  
Data Access ◽  
Geospatial Data ◽  
Combination Method ◽  
System Throughput ◽  
Time Interval ◽  
Cache Replacement ◽  
Access Pattern ◽  
Replacement Strategy ◽  
Hit Rate ◽  
Access Patterns

Cache replacement strategy is the core for a distributed high-speed caching system, and effects the cache hit rate and utilization of a limited cache space directly. Many reports show that there are temporal and spatial local changes in access patterns of geospatial data, and there are popular hot spots which change over time. Therefore, the key issue for cache replacement strategy for geospatial data is to get a combination method which considers both temporal local changes and spatial local changes in access patterns, and balance the relationship between the changes. And the cache replacement strategy should fit the distribution and changes of hotspot. This paper proposes a cache replacement strategy based on access pattern which have access spatiotemporal localities. Firstly, the strategy builds a method to express the access frequency and the time interval for geospatial data access based on a least-recently-used replacement (LRU) algorithm and its data structure; secondly, considering both the spatial correlation between geospatial data access and the caching location for geospatial data, it builds access sequences based on a LRU stack, which reflect the spatiotemporal locality changes in access pattern. Finally, for achieving the aim of balancing the temporal locality and spatial locality changes in access patterns, the strategy chooses the replacement objects based on the length of access sequences and the cost of caching resource consumption. Experimental results reveal that the proposed cache replacement strategy is able to improve the cache hit rate while achieving a good response performance and higher system throughput. Therefore, it can be applied to handle the intensity of networked GISs data access requests in a cloud-based environment.

scholarly journals SqORAM: Read-Optimized Sequential Write-Only Oblivious RAM

2020 ◽  
Vol 2020 (1) ◽  
pp. 216-234
Author(s):  
Anrin Chakraborti ◽  
Radu Sion
Keyword(s):  
Random Access ◽  
Data Access ◽  
Data Locality ◽  
Storage Device ◽  
Random Data ◽  
Locality Property ◽  
Oblivious Ram ◽  
Locality Preserving ◽  
Access Patterns ◽  
Access Data

AbstractOblivious RAMs (ORAMs) allow a client to access data from an untrusted storage device without revealing the access patterns. Typically, the ORAM adversary can observe both read and write accesses. Write-only ORAMs target a more practical, multi-snapshot adversary only monitoring client writes – typical for plausible deniability and censorship-resilient systems. This allows write-only ORAMs to achieve significantly-better asymptotic performance. However, these apparent gains do not materialize in real deployments primarily due to the random data placement strategies used to break correlations between logical and physical names-paces, a required property for write access privacy. Random access performs poorly on both rotational disks and SSDs (often increasing wear significantly, and interfering with wear-leveling mechanisms).In this work, we introduce SqORAM, a new locality-preserving write-only ORAM that preserves write access privacy without requiring random data access. Data blocks close to each other in the logical domain land in close proximity on the physical media. Importantly, SqORAM maintains this data locality property over time, significantly increasing read throughput.A full Linux kernel-level implementation of SqORAM is 100x faster than non locality-preserving solutions for standard workloads and is 60-100% faster than the state-of-the-art for typical file system workloads.

scholarly journals An Adaptive Data Placement Architecture in Multicloud Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Pengwei Wang ◽  
Caihui Zhao ◽  
Yi Wei ◽  
Dong Wang ◽  
Zhaohui Zhang
Keyword(s):  
Service Providers ◽  
Cloud Service ◽  
Data Access ◽  
Data Retrieval ◽  
Data Placement ◽  
Data Availability ◽  
Maintenance Cost ◽  
Time Varying ◽  
Access Pattern ◽  
Data Lifetime

Cloud service providers (CSPs) can offer infinite storage space with cheaper maintenance cost compared to the traditional storage mode. Users tend to store their data in geographical and diverse CSPs so as to avoid vendor lock-in. Static data placement has been widely studied in recent works. However, the data access pattern is often time-varying and users may pay more cost if static placement is adopted during the data lifetime. Therefore, it is a pending problem and challenge of how to dynamically store users’ data under time-varying data access pattern. To this end, we propose ADPA, an adaptive data placement architecture that can adjust the data placement scheme based on the time-varying data access pattern and subject for minimizing the total cost and maximizing the data availability. The proposed architecture includes two main components: data retrieval frequency prediction module based on LSTM and data placement optimization module based on Q-learning. The performance of ADPA is evaluated through several experimental scenarios using NASA-HTTP workload and cloud providers information.

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