What is secure multiparty computation helps in privacy ?
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Secure multiparty computation enables the secure processing of data from multiple users without exposing the data or computation code to other parties or computation hardware providers. This approach allows for new applications and improved data computation results by securely and privately sharing data, creating richer datasets for analysis . It is achieved through coding the secrecy data into secrecy vectors, converting the secrecy comparison between parties into a partial scalar product problem . Secure multiparty computation has various applications, including privacy-preserving auctions, private DNA comparisons, private machine learning, and threshold cryptography . However, the deployment of secure computation still requires expertise, and further research breakthroughs are needed to make it practical for large datasets and complex problems, as well as user-friendly for non-experts . Recent developments in computer processors and the popularization of Trusted Execution Environment functionality have made secure multiparty computation possible .
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| Papers (4) | Insight |
|---|---|
| Secure multiparty computation helps in preserving the privacy of data among multiple participants by using secrecy coding and converting secrecy comparison to scalar product problem. | |
15 Nov 2022 | Secure multiparty computation enables secure processing of data from multiple users without exposing the data or computation code to other parties, ensuring privacy. |
| Secure multiparty computation helps in preserving data privacy by enabling multiple parties to perform computations on their data without revealing sensitive information. | |
| Secure multiparty computation (MPC) enables parties to compute a joint function of their private inputs while preserving privacy. |
Related Questions
What are the most common types of vulnerabilities in secure multiparty computing systems?5 answersThe most common types of vulnerabilities in secure multiparty computing systems are active corruption, passive corruption, fail-corruption, and omission corruption. Active corruption refers to the adversary having full control over the corrupted player, while passive corruption means the adversary can see what the corrupted player sees. Fail-corruption allows the adversary to force the corrupted player to crash irrevocably. Omission corruption, on the other hand, allows the adversary to selectively block messages sent from and to the corrupted player without actually seeing the message. These vulnerabilities have been studied extensively in the context of secure multiparty computation, with different security definitions and protocols proposed to tolerate them.
What types of attacks are most commonly performed in multiparty computing systems?4 answersThe most commonly performed attacks in multiparty computing systems include poisoning attacks, active attacks, fault attacks, and side-channel attacks. Poisoning attacks involve tampering with the training data to increase the classification error of the constructed hypothesis/model. Active attacks occur when an adversary has malicious control over the communications of certain users during the key exchange process. Fault attacks exploit vulnerabilities in shared computing, such as overdrive fault attacks on Graphics Processing Units (GPUs) that can cause silent data corruptions. Side-channel attacks, like the Prime+Probe attack, exploit timing side-channels to extract sensitive information. These attacks highlight the need for robust security measures in multiparty computing systems to protect against various types of threats.
How do these attacks affect the security and privacy of the computations performed in multiparty computing systems?4 answersMultiparty computing systems are vulnerable to attacks that can compromise the security and privacy of the computations performed. These attacks can lead to the theft of privacy data and the leakage of sensitive information. Traditional privacy protection technologies are facing failure in the face of these attacks, necessitating the need for new approaches. One proposed solution is the use of secure multiparty computation protocols, which enable the distribution of sensitive data among multiple parties while preventing them from gaining much information about the data. These protocols ensure that only a trusted third party can view the final results of the computations. Additionally, the use of blockchain technology can further enhance the security and privacy of multiparty computations by removing the reliance on third parties and allowing for safe and fair joint computation of data.
What are the challenges and limitations of using secure multiparty computation for health care data analytics?5 answersSecure multiparty computation (SMC) has challenges and limitations when used for health care data analytics. The complexity of SMC code makes it difficult to determine if security properties and correctness guarantees hold in practice. Formal methods have been used to provide guarantees for SMC systems, but these have mostly been applied to domain-specific languages (DSL) and not general-purpose approaches. Additionally, legal and ethical restrictions on accessing relevant health care data hinder data science research. Synthetic data generation algorithms with privacy guarantees have emerged as a solution, but existing approaches rely on data holders supplying raw data to a trusted curator. This limits applicability as valuable data is often locked up in silos controlled by entities who cannot share their data without privacy concerns. To overcome this, a solution is proposed where data holders only share encrypted data for differentially private synthetic data generation, using secure multiparty computation (MPC) computations performed on encrypted data.
How does homomorphic encryption help to protect privacy?5 answersHomomorphic encryption helps to protect privacy by allowing operations to be performed on encrypted data without decrypting it. This enables sensitive data, such as images or machine learning models, to be processed and analyzed while remaining encrypted, thus preserving privacy. Homomorphic encryption has been used in various applications, including watermarking services on blockchain-driven platforms, secure outsourced image processing in public cloud servers, and multi-party privacy-protected machine learning. By combining homomorphic encryption with machine learning, users can perform artificial intelligence tasks without disclosing their private data. Additionally, homomorphic encryption has been applied in the design and implementation of privacy-preserving mechanisms for real-time energy markets, ensuring the protection of participants' privacy.
How can federated learning be used to protect privacy?5 answersFederated learning can be used to protect privacy by enabling multiple organizations to train a model without sharing their data with a central server. This approach addresses the privacy concerns associated with sharing sensitive medical data. One method to protect privacy in federated learning is through the use of encrypted computations and secure multiparty computation, which allow for the analysis of data without revealing the raw data itself. Another approach is the use of privacy-preserving techniques such as parameter-based encrypted federated learning secure aggregation and watermark-based intellectual property protection schemes. Additionally, the concept of anonymity sets, differential privacy, and public bulletin boards can be leveraged to support private and anonymous dynamic participation in federated learning, ensuring the protection of user anonymity. These techniques and frameworks provide privacy guarantees while allowing for collaborative learning on sensitive data.