scispace - formally typeset
Search or ask a question

What are asymmetric keys in cryptography? 


Best insight from top research papers

Asymmetric keys in cryptography, also known as public-key cryptography, involve the use of two keys - a public key and a private key . The public key is utilized for encryption, while the private key is used for decryption. Unlike symmetric key cryptography where the same key is used for both encryption and decryption, asymmetric key cryptography employs a matched pair of keys for each user. This method is slower but is well-suited for key management and is central to many real-world cryptographic systems. Various algorithms such as RSA, Diffie-Hellman, ElGamal, and others fall under asymmetric key cryptography, enhancing security by using different keys for encryption and decryption purposes. Asymmetric key algorithms play a crucial role in securely transmitting data and ensuring authentication without the need for parties to agree on a secret key beforehand.

Answers from top 5 papers

More filters
Papers (5)Insight
Book ChapterDOI
01 Jan 2022
Asymmetric keys in cryptography refer to public and private keys used in public-key cryptography, where the public key encrypts data and the private key decrypts it.
Asymmetric keys in cryptography involve a matched pair of keys (public and private) for each user, unlike symmetric keys. They are essential for key management in cryptographic systems.
Asymmetric keys in cryptography are used for encryption and decryption with different keys: Public Key for encryption and Private Key for decryption, ensuring secure data transmission without shared secret keys.
Asymmetric keys in cryptography refer to using two different keys - a public key for encryption and a private key for decryption, enhancing security.
Book ChapterDOI
01 Jan 2023
Asymmetric keys in cryptography are a pair of keys - public and private. Public key is shared for encryption, while private key is kept secret for decryption.

Related Questions

What is the difference between symmetry cell and asymmetry cell?5 answersSymmetric cell division results in two daughter cells with the same fate, while asymmetric cell division produces daughter cells with different fates. Asymmetric division plays a crucial role in generating cell diversity and maintaining stem cell populations. Epigenetic mechanisms are implicated in maintaining or altering cell fate during asymmetric stem cell division, highlighting the importance of asymmetry in influencing cell identity. In biological structures, symmetry and asymmetry are fundamental aspects. While symmetric structures are simpler to encode and may arise more commonly due to random mutations, evolutionary pressures often drive the development of asymmetry in complex structures like the human brain, enhancing multitasking abilities and energy efficiency. Asymmetry in biological membranes, driven by lipid composition differences, influences core biological functions and structural properties.
What are important of Asymmetric carbon carbon bond?5 answersAsymmetric carbon-carbon bond formation is important in various fields. In the field of organic chemistry, it enables the rapid construction of therapeutic small molecules, which is crucial for improving human health. Asymmetric carbonylation reactions, such as asymmetric hydroformylation, are attractive methods for synthesizing chiral compounds on a large scale. Catalytic asymmetric carbon-carbon bond-forming reactions provide efficient ways to synthesize optically active compounds, although their efficiency in terms of turnover number can be a challenge. Solventless asymmetric alkylation in a ball mill has been shown to give amino acid precursors with stereochemical control. Catalytic asymmetric transformations based on carbon-carbon bond cleavages have emerged as versatile strategies for accessing transient organometallic species.
How does monetary policy affect financial stability in an asymmetric manner?5 answersMonetary policy can affect financial stability in an asymmetric manner. One way is through the use of macroprudential interventions in financial markets, which can correct aggregate demand externalities and address wealth distribution effects on output. Another way is through the focus on price stability as the paramount objective of monetary policy, which can promote financial stability by avoiding fluctuations in the rate of inflation. Additionally, the use of macroprudential measures alongside monetary policy can address financial stability risks, with the two instruments working together to achieve the objectives of inflation targeting and maintaining financial stability. The interaction between monetary and financial trilemmas highlights the need for policies that can address the asymmetric effects of monetary policy on financial stability.
How does monetary policy affect financial stability in an asymmetric environment?5 answersMonetary policy has different effects on financial stability in an asymmetric environment. In an economy with asymmetric information between borrowers and lenders, monetary policy affects the cost of external finance, leading to real effects in the flexible-price equilibrium. The natural rate of interest, which is often used as a policy indicator, is not useful in this context as it is itself affected by monetary policy. A generalized definition of the natural rate that is not policy dependent can be used as the intercept of a monetary policy rule to achieve price stability. The impact of monetary policy shocks on house prices also depends on the nature of the shock and local housing supply elasticities. Expansionary monetary policy shocks have a larger impact on house prices in supply-inelastic areas, while contractionary shocks have a greater impact in supply-elastic areas. In an asymmetric environment, the optimal design of monetary policy takes into account structural asymmetries across countries and the heterogeneity of economic structures to achieve welfare gains.
What is asymmetric key encryption for data security and privacy preservation in cloud computingvation in cloud computing?4 answersAsymmetric key encryption is a technique used for data security and privacy preservation in cloud computing. It involves the use of public key cryptography to protect sensitive data. The security key, which is the most critical factor in protecting public key cryptographic data encryption, is not obtained by the server. This approach can be used in Cryptographically Enforced Access Control method, where either symmetric key or asymmetric key mode is used. The use of asymmetric key encryption ensures that the data is securely stored and accessed in the cloud environment, providing better protection levels for key management. It also allows for secure data sharing and efficient storage in the cloud. By using asymmetric key encryption, data owners can have confidence that their private data will not be exposed or mishandled by cloud providers.
Is symmetric encryption more efficient than asymmetric encryption?5 answersSymmetric encryption is generally considered to be more efficient than asymmetric encryption. This is because symmetric encryption requires less computational power and resources compared to asymmetric encryption. In symmetric encryption, the same key is used for both encryption and decryption, which simplifies the process and reduces the computational overhead. On the other hand, asymmetric encryption involves the use of a public key for encryption and a private key for decryption, which requires more computational resources and time. However, it is important to note that while symmetric encryption is more efficient, asymmetric encryption offers additional security benefits such as identity authentication and integrity of encrypted messages. Therefore, the choice between symmetric and asymmetric encryption depends on the specific requirements of the application and the desired balance between efficiency and security.

See what other people are reading

How does power measurement contribute to the security of IoT devices?
10 answers
Power measurement plays a pivotal role in enhancing the security of Internet of Things (IoT) devices by enabling the detection and analysis of cyber threats through the observation of power consumption patterns. The metrological approach to security testing of embedded devices, including IoT transducers, emphasizes the importance of power analysis to ensure data confidentiality and integrity. This method relies on statistical analysis and design of experiments to accurately assess device security, particularly through scatter attacks in power analysis. Similarly, the security of terminal devices in power systems, which are integral to the IoT, can be monitored by collecting power consumption side-channel information. This approach uses supervised learning algorithms to identify abnormalities in device behavior, indicating potential cyber threats. Furthermore, the study of non-intrusive side-channel power data for intrusion detection highlights how power behavior analysis under normal and attack conditions can reveal distinct power signatures associated with various cyber attacks. This knowledge allows security teams to reverse engineer attacks and gain a deeper understanding of breach events, enhancing the overall security posture of IoT devices. Power analysis side-channel attacks, which exploit power consumption data to uncover cryptographic keys, underscore the vulnerability of cryptographic implementations in IoT devices. Countermeasures against such attacks, despite their trade-offs, are crucial for maintaining the security of these devices. In the context of IoT medical transducers, power measurement is utilized to assess the vulnerability of devices to cyber threats. By implementing countermeasures that increase the difficulty of discovering cryptographic keys through power analysis, the security of IoT medical devices is significantly enhanced. Collectively, these studies demonstrate the critical role of power measurement in identifying vulnerabilities, monitoring for potential threats, and implementing effective countermeasures to secure IoT devices against cyber attacks.
What is aes algorithm?
4 answers
The Advanced Encryption Standard (AES) algorithm is a widely utilized symmetric block cipher known for its robust encryption capabilities. It offers various key sizes, including 128, 192, and 256 bits, with a fixed block size of 128 bits. AES ensures secure data transmission over the internet by converting information into encrypted formats that are extremely challenging for hackers to decrypt. This algorithm is highly regarded for its military-grade security, making data extraction nearly impossible. AES can be efficiently implemented in both hardware and software, providing high security, data integrity, and fast encryption and decryption processes. Overall, AES stands out as a reliable and effective encryption method for safeguarding sensitive information in various applications.
How can neural network be used to derive a preditive model from available data?
5 answers
Neural networks can be utilized to derive predictive models from available data through various techniques outlined in different research papers. For instance, neural networks can be employed for financial risk evaluation by constructing evaluation index systems and utilizing entropy weight TOPSIS methods. Additionally, in the context of mobile network operators, neural networks can predict malfunctions by analyzing and preparing data from 4G+ networks. Furthermore, neural networks can be enhanced by incorporating pre-treatment techniques like data smoothing and frequency content analysis to improve model accuracy over operating ranges. Moreover, in the realm of privacy protection, neural networks can predict sensitive information securely through homomorphic encryption algorithms, ensuring data privacy and prediction accuracy.
What is Hybrid Intelligence?
7 answers
Hybrid Intelligence represents a forward-thinking approach that synergizes human and artificial intelligence (AI) to solve complex tasks, leveraging the unique strengths of both entities to achieve outcomes neither could independently. This concept is rooted in the cooperative collaboration between humans and AI, aiming to augment human decision-making rather than replace it, thereby addressing the limitations of current AI systems which lack social and emotional intelligence and are restricted in their understanding of reality. The foundational ideas behind Hybrid Intelligence can be traced back to pioneers like William Ross Ashby and Douglas Carl Engelbart, emphasizing the importance of modeling human perception and enhancing the interaction between humans and the digital world. Hybrid Intelligence is not just theoretical but has practical applications across various domains. For instance, in the service industry, it has been shown to aid in decision-making and reduce work stress, demonstrating the potential of AI to augment human capabilities without fostering overreliance. Similarly, in healthcare, particularly in telemedicine, Hybrid Intelligence can secure data communication, showcasing its utility in sensitive fields. The concept also extends to the realm of analytics, where it enhances the ability of organizations to understand and predict trends by combining human insight with AI's analytical capabilities. Moreover, the development of Hybrid Intelligence systems necessitates interpretability in AI decisions, ensuring that humans can understand and trust AI-generated recommendations. This is crucial for fostering effective collaboration between humans and AI, as seen in conversational AI applications that support knowledge management within organizations. The ultimate goal of Hybrid Intelligence is to create a symbiotic relationship where both human and machine intelligences are not only integrated but also co-evolve, leading to innovative solutions for complex societal and environmental challenges.
How to protect brand reputation from unauthorized sales channels in footwear industry.?
5 answers
To safeguard brand reputation from unauthorized sales channels in the footwear industry, various anti-counterfeiting measures can be implemented. Utilizing technologies like pre-stressed frangible coatings for tamper indicating, embedding unique identification codes in electronic tags for shoes, and incorporating indicia holding mechanisms in clothing articlescan help in authentication. Additionally, employing methods such as reading unique barcodes on products for direct authenticity verificationand empowering consumers through Routine Activity Theory to differentiate between counterfeit and genuine products onlinecan enhance brand protection. By combining these strategies, brands can mitigate the risks posed by unauthorized sales channels and maintain their reputation in the market.
What is the motivations, abilities and oppeotunities theory?
4 answers
The Ability-Motivation-Opportunity (AMO) theory is a framework that examines the factors influencing individual performance and behavior. AMO theory suggests that a combination of an individual's abilities, motivations, and opportunities can determine their performance. This theory has been applied in various contexts, such as in understanding consumer behavior related to food waste reduction, stimulating employee creativity in digital work environments, and exploring the impact of furlough on employees during the Covid-19 pandemic. The AMO framework highlights the importance of considering individual characteristics, external opportunities, and internal motivations when analyzing performance outcomes and behavior, making it a versatile tool for studying human behavior in different settings.
What is the motivations, abilities and oppeotunities theory in entreprenruship?
4 answers
The Ability-Motivation-Opportunity (AMO) theory, originally applied in the HRM field, focuses on how individual abilities, motivations, and opportunities influence performance outcomes. In the context of entrepreneurship, this theory can be crucial in understanding the factors that drive entrepreneurial success. For instance, in projects, the AMO framework can help identify key factors that foster improved workforce performance. Additionally, the theory of achievement motivation distinguishes different types of motivation, such as task-involvement, ego-involvement, and extrinsic involvement, which can impact entrepreneurial behavior and success. Moreover, in consumer behavior related to reducing food waste, motivations, opportunities, and abilities play a significant role in influencing behaviors and outcomes, highlighting the importance of considering these factors in entrepreneurial contexts as well.
How does cloud computing impact patient data security and privacy in the healthcare industry?
5 answers
Cloud computing significantly impacts patient data security and privacy in the healthcare industry by offering secure storage options and robust authentication mechanisms. The utilization of cloud technology ensures the protection of sensitive healthcare data through encryption techniques like Rail Fence Data Encryption (RFDE) algorithm, enhancing privacy standards. Cloud-based virtual servers aid in storing patient protected health information (PHI) securely, facilitating quick diagnoses and minimizing healthcare expenses. Moreover, the integration of artificial intelligence techniques enhances healthcare data security in the cloud computing environment, providing improved protection against data breaches and unauthorized access. Overall, cloud computing in healthcare ensures efficient data management while addressing privacy concerns and enhancing security measures for patient information.
How does machine learning impact the security of cryptosystems?
5 answers
Machine learning (ML) plays a crucial role in enhancing the security of cryptosystems. By leveraging ML techniques, such as Logistic Regression, Random Forest Classifier, and XGB Classifier, fraudulent activities within the cryptocurrency industry can be effectively detected with high accuracy. Additionally, ML can aid in the development of blockchain applications, improving their security. Furthermore, in the realm of digital data security, ML, specifically through Support Vector Machine (SVM), can be utilized as a tool for identifying the security levels of encryption algorithms, ensuring the selection of the most suitable encryption strategy to defend against attacks. These findings underscore the significant impact of ML on bolstering the security of cryptosystems and combating potential threats effectively.
What advantages brings the implementation of KG while LLM-Inference?
5 answers
The implementation of Knowledge Graphs (KG) alongside Large Language Models (LLMs) in inference processes offers several advantages. KGs provide a structured, transparent, and collaborative way to organize knowledge across various domains, enhancing the effectiveness of information representation. When integrated with LLMs, KGs can support Knowledge Graph Engineering (KGE) by leveraging the capabilities of models like ChatGPT for the development and management of KGs. Additionally, the combination of LLMs and KGs can enhance information extraction, reasoning, and question-answering tasks, as demonstrated by the outperformance of GPT-4 over ChatGPT in various tasks related to KG construction and reasoning. Moreover, optimizing the transformer architecture with privacy-computing friendly approximations can significantly reduce private inference costs while maintaining model performance, further enhancing the advantages of KG-LLM integration.
How to predict driver and passenger matching probabilities?
5 answers
To predict driver-passenger matching probabilities accurately, a Multi-View model (MV) is proposed, which considers dynamic features of passengers, drivers, trip orders, and context interactions. This model addresses the challenge of decision-making for two-party systems like ride-hailing platforms, where dynamics from both driver and passenger sides must be considered simultaneously. Additionally, a Knowledge Distillation framework (KD) is designed to enhance predictive power, especially in smaller cities with limited data, by leveraging insights from cities with denser data. By conducting experiments on real-world datasets from various cities, the effectiveness of this approach in predicting matching success rates is demonstrated, showcasing the superiority of the proposed solution in enhancing the matching process on ride-hailing platforms.