scispace - formally typeset
Search or ask a question

What are the problems with dynamic searchable encrytion? 


Best insight from top research papers

Dynamic searchable encryption (DSE) faces several problems. Firstly, existing DSE schemes based on public key cryptography are complex and computationally expensive, making them inefficient for practical applications . Secondly, traditional Oblivious Random Access Machine (ORAM) techniques used to hide access patterns in DSE incur significant communication overhead and cannot hide search patterns . Thirdly, leakage of search patterns and access patterns in general DSE schemes makes them vulnerable to statistical attacks . Additionally, the leakage of data update information in dynamic searchable symmetric encryption (DSSE) schemes compromises data privacy . Finally, there is a trade-off between security and efficiency in DSE schemes, with stronger security often resulting in decreased efficiency .

Answers from top 4 papers

More filters
Papers (4)Insight
The paper discusses the problem of data update leakage in dynamic searchable encryption schemes. It proposes forward and backward secure SSE schemes to address this issue.
The paper states that the general dynamic searchable symmetric encryption (DSSE) scheme is vulnerable to statistical attacks due to the leakage of search patterns and access patterns.
The paper discusses that existing dynamic searchable encryption schemes are vulnerable to statistical attacks due to the leakage of both search patterns and access patterns.
The problems with dynamic searchable encryption are information abuse and file injection attacks, as well as complicated construction and high computational overhead.

Related Questions

What are the main challenges and opportunities in the design of dynamic electricity capacity markets and dynamic network tariffs?5 answersThe main challenges in the design of dynamic electricity capacity markets and dynamic network tariffs include addressing the evolving electricity pricing mechanisms and the integration of intermittent renewable energy generation (IREG). Another challenge is accurately predicting customers' demand to participate in the wholesale market and developing proper tariff mechanisms considering other retailers' behavior to maximize profit. Additionally, there is a need for a smooth transition from traditional schemes to new technologies, which requires solutions that can be implemented in the current circumstances. On the other hand, the opportunities in the design of dynamic electricity capacity markets and dynamic network tariffs include the potential to balance supply and demand, limit costs of grid stability, support renewable energy integration, increase overall economic efficiency, and provide financial incentives for demand response and load shifting.
What are the advantages and disadvantages of making a system dynamic?5 answersMaking a system dynamic has several advantages. Firstly, it allows for the simplicity and transparency of complex problems, revealing the underlying structure of the system. Secondly, it helps in understanding the structure and dynamics of complex systems, enabling the design of high-leverage policies for sustained improvement. Additionally, it facilitates learning and policy design through the development of formal models and management flight simulators. However, there are also disadvantages to making a system dynamic. One challenge is the difficulty of experimental testing in human systems, which often require the active participation of individuals without technical training. Another challenge is the credit-assignment problem, where learners struggle to associate outcomes with earlier actions in dynamic systems. Furthermore, the dynamic nature of systems presents a challenge in creating software-intensive systems that can adapt and evolve in a changing reality. Overall, while making a system dynamic offers advantages in understanding and improving complex systems, it also poses challenges in testing and learning within these systems.
What is dynamic network analysis?5 answersDynamic network analysis refers to the study of networks that change over time. It involves analyzing the structure and evolution of networks where nodes and edges can vary over different time periods. Dynamic network analysis has been applied in various fields such as protein structural classification, complex network dynamics, social network analysis, financial network analysis, and climate network analysis. In protein structural classification, dynamic network models have been proposed to improve the performance of existing approaches. In complex network dynamics, bibliometrics has been used to analyze the development and current status of dynamic complex networks. In social network analysis, two-mode networks and hypergraphs have been used to uncover the structure and evolution of social networks. In financial network analysis, state-space models have been used to estimate the dynamic network among financial institutions. In climate network analysis, network science has been applied to study the interactions and dynamics of the global climate system.
What are the challenges in dynamic site layout planning for construction sites?5 answersDynamic site layout planning for construction sites faces several challenges. These challenges include the need to consider changing constraints such as site constraints, component constraints, construction schedule, and component arrival schedule that may interact and change over time. Another challenge is the lack of research in the area of dynamic planning of construction sites, with most literature focusing on static site layout models. Additionally, the size and budget of linear infrastructure projects make them more sensitive to dynamic planning, as different sequences in construction methods can lead to significant changes in cost fluctuations. Furthermore, the process of identifying the number and size of temporary facilities needed throughout the construction process and locating them to satisfy geometrical and safety constraints is crucial but can be challenging. Finally, the dynamic nature of construction site layout planning itself adds complexity and inefficiency, which can be addressed through the use of 4D BIM modeling.
How technology develop dynamic learning?3 answersTechnology can improve dynamic learning by providing visual models of mathematical concepts, guiding teachers in developing student reasoning, enhancing the interaction between teachers and students, and facilitating multimodal interactions that make reasoning processes more explicit. Through dynamic representations, technology helps build conceptual understanding and has been shown to be effective in enhancing learning. Teachers can benefit from guidance in using technology to develop student reasoning across a series of lessons. Technology also improves the dynamics between teachers and students, leading to enhanced learning experiences. Additionally, technology-mediated multimodal interactions support rich educational dialogues and highlight differences between participants' perspectives, making reasoning processes more explicit. Overall, technology plays a crucial role in developing dynamic learning environments by providing tools and resources that enhance conceptual understanding, support teacher guidance, and facilitate interactive and multimodal learning experiences.
Why is language dynamic?2 answersDynamic languages, such as Python and Ruby, have gained popularity due to their unique characteristics and history. These languages are interesting because they differ from statically typed languages in several ways. They allow for dynamic parallelization, which finds parallelism during program execution and can uncover more parallelism than is statically possible at compile time. Dynamic languages also support dynamic resolution, which detects shared data and coordinates access to it at runtime, enabling parallelization of non-trivial functions. Additionally, dynamic languages utilize dynamic granularity estimation to decide when parallel evaluation of an expression will speed up program execution. Furthermore, these languages support concurrent garbage collection, which allows for the parallel reclamation of spent storage during program computation. Overall, the dynamic nature of these languages enables flexibility, adaptability, and efficient execution.