Privacy-Preserving Access of Outsourced Data via Oblivious RAM Simulation (1007.1259v2)

Abstract: Suppose a client, Alice, has outsourced her data to an external storage provider, Bob, because he has capacity for her massive data set, of size n, whereas her private storage is much smaller--say, of size O(n^{1/r}), for some constant r > 1. Alice trusts Bob to maintain her data, but she would like to keep its contents private. She can encrypt her data, of course, but she also wishes to keep her access patterns hidden from Bob as well. We describe schemes for the oblivious RAM simulation problem with a small logarithmic or polylogarithmic amortized increase in access times, with a very high probability of success, while keeping the external storage to be of size O(n). To achieve this, our algorithmic contributions include a parallel MapReduce cuckoo-hashing algorithm and an external-memory dataoblivious sorting algorithm.

• The paper proposes Oblivious RAM (ORAM) simulations with reduced logarithmic or polylogarithmic access time overhead for privacy-preserving access to outsourced data.
• It utilizes innovative techniques like a parallel MapReduce cuckoo hashing algorithm and an external-memory data-oblivious sorting algorithm to improve ORAM efficiency.
• The findings offer a significant stride towards practical and efficient ORAM use, with profound implications for data privacy in cloud storage and outsourced data processing.

Privacy-Preserving Access of Outsourced Data via Oblivious RAM Simulation

The paper "Privacy-Preserving Access of Outsourced Data via Oblivious RAM Simulation" by Michael T. Goodrich and Michael Mitzenmacher presents a comprehensive examination of techniques for maintaining data privacy when operating over outsourced storage, particularly through the lens of Oblivious RAM (ORAM) simulation. This work focuses on devising methods by which a client can interact with outsourced data while ensuring that access patterns remain confidential, even when the data provider cannot be fully trusted.

Overview and Contributions

The core problem addressed is the privacy challenge inherent in maintaining confidentiality of data access patterns, even when the specific content is encrypted. Traditional encryption schemes suffice for protecting data contents but fall short when considering access patterns, which themselves can reveal sensitive information. The authors propose ORAM schemes with logarithmic or polylogarithmic amortized access time overhead, a significant theoretical advancement in the field.

A pivotal contribution of the paper is the reduction of ORAM access overhead, which historically had been computationally expensive. The authors achieve this by utilizing innovations such as a parallel MapReduce cuckoo hashing algorithm and an external-memory data-oblivious sorting algorithm. The use of hierarchical hashing results in efficient organization and retrieval of data, facilitating the desired oblivious access patterns with high probability of success.

Detailed Analysis

The simulation uses a hierarchy of hash tables, which aids in the seamless management and retrieval of data. Each hash table level operates under the assumption of minimal private storage on the client's side, emphasizing the storage capability leveraged at the provider's end. A notable analytical focus is the balance between storage overhead and access time, which the authors optimize to $O(n)$ storage and $O(\log^2 n)$ access time when using constant-sized private memory.

Specific algorithmic tactics include the employment of cuckoo hashing, notable for facilitating efficient data placement and retrieval through a constant-time complexity per operation, and this is pivotal in achieving the reported ORAM efficiency gains. Moreover, a log-sized stash is utilized to handle insertion anomalies gracefully, a crucial aspect ensuring the stability of cuckoo hashing under the assumptions of the treatment.

Results and Implications

The findings underscore a significant stride toward practical and efficient ORAM simulations. By reducing overheads and ensuring high-probability success rates, the results are highly promising for real-world applications where large datasets are outsourced, and privacy is paramount.

The implications of this work are profound in both theoretical and practical domains. From a theoretical standpoint, it advances the understanding of data privacy mechanisms with minimal computational overhead. Practically, this has the potential to reshape data privacy standards in industries reliant on cloud storage and outsourced data processing capabilities.

Future Directions

Future research can delve into practical implementations of these theoretical constructs, exploring the efficacy of these ORAM simulations under varied operational constraints and extending these techniques to handle dynamic data structures with mutable entries. Furthermore, the exploration of cryptographic assumptions underlying the simulations, such as random hash functions, presents an additional avenue for enhancement in achieving robust, yet practical, data privacy models.

Overall, the paper adeptly balances rigorous theoretical development with considerations for practical applicability, contributing valuable methodologies for enhancing privacy in outsourced data environments.