Blockchain as a Service: A Decentralized Secure Computing Paradigm

Table of Contents

1. Gabatarwa

With advancements in sensing and computing technologies, data-driven approaches (particularly machine learning) have become indispensable across various domains. However, two major challenges persist: acquiring large-scale datasets and ensuring sufficient computational resources. This often leads to reliance on centralized cloud service providers, raising concerns about transparency, security, and privacy. In fields such as healthcare, regulatory restrictions prevent data sharing with third parties. This paper proposes a decentralized secure computing paradigm leveraging blockchain, homomorphic encryption, and software-defined networking (SDN) to enable privacy-preserving collaboration among distributed and untrusted computational nodes.

2. Baya da Ayyukan Da Alaka

2.1 Fasahar Blockchain

Blockchain wani ne marar canzawa, maras tsakiyar cibiyar lissafin dijital, wanda ke hade da tubalan da suka haɗa da hanyoyin sirri. Kowane tubali yana ɗauke da ƙimar hash na tubalin da ya gabata, bayanan ma'amala, da alamar lokaci, yana tabbatar da cikakkiyar bayanai, kuma yana ba da damar amincewa tsakanin mahalarta ba tare da buƙatar ikon tsakiya ba.

2.2 Koyon Injiniya na'ura mai kwakwalwa mara tsakiya

Federated Learning da Google ta gabatar tana ba da damar horar da samfuri akan bayanai masu rarrabuwa. Duk da haka, tana buƙatar wakili na tsakiya mai daidaitawa, wanda zai iya zama cikas na bazuwar guda. Hanyarmu ta kawar da wannan matsalar ta hanyar amfani da blockchain don samun sarrafa kai.

2.3 Homomorphic Encryption

Homomorphic encryption enables computation on encrypted data without decryption, thereby protecting privacy. For example, given two encrypted values $E(a)$ and $E(b)$, one can directly compute $E(a + b)$. This is crucial for secure aggregation in decentralized learning.

3. Proposed Paradigm

3.1 System Architecture

The system comprises multiple computational nodes, a blockchain network, and an SDN controller. Nodes participate in local model training, with updates being aggregated through smart contracts on the blockchain. Homomorphic encryption ensures data remains private during the aggregation process.

3.2 Technical Implementation

Wannan tsarin ya haɗa fasahohi da yawa:

• Blockchain:Ta hanyar kwangila mai hankali don sarrafa sabunta samfuri da ƙarfafa ƙarfafawa.
• Homomorphic encryption:Kare ya amfani da bayanai da tarawa, a kiyaye amincin bayanai. Tsarin ɓoyayyen bayanai yana ba da damar aiki kamar $c_1 = E(m_1)$ da $c_2 = E(m_2)$ su haɗu zuwa $c_3 = c_1 \oplus c_2$, inda $\oplus$ ke nufin ƙari mai kama.
• SDN:Inganta hanyar sadarwa, don samun ingantaccen musayar bayanai tsakanin nodes.

4. Sakamakon Gwaji

4.1 Saitin Kwaikwayo

The experiment was conducted using a network comprising 100 nodes with varying computational capabilities. The dataset consisted of 50,000 samples for a classification task. The blockchain was simulated using a Proof of Work consensus mechanism.

4.2 Performance Metrics

Key metrics include accuracy, communication overhead, and privacy protection. The proposed method achieves 92% accuracy, comparable to centralized methods, and reduces communication overhead by 15% due to SDN optimization. Privacy is fully protected as raw data never leaves the nodes.

5. Code Implementation

Here is an example of aggregation pseudocode based on homomorphic encryption:

// Secure aggregation pseudocode

6. Future Applications

The proposed paradigm can be applied to:

• Healthcare:Collaborative model training between hospitals using patient data without sharing raw data, compliant with regulations such as HIPAA.
• Autonomous Driving:Decentralized learning utilizing data from multiple vehicles to enhance navigation models.
• IoT Networks:Ingantacciyar tattara bayanan firikwensin a cikin Intanet na Abubuwa na Masana'antu don kulawa na tsinkaya.
• Sabis na Kuɗi:Horar da samfurin gano zamba bisa bayanan bankuna da yawa ba tare da fallasa bayanan sirri ba.

Aikin nan gaba zai mayar da hankali kan faɗaɗa tsarin zuwa babbar hanyar sadarwa, haɗa sauran hanyoyin yarjejeniya (kamar Proof of Stake), da inganta tsarin ɓoyayyen bayanai don haɓaka inganci.

7. Bincike na Asali

Takardar "Blockchain a matsayin Sabis: Tsarin Lissafi na Tsaro na Rarrabawa" ta gabatar da wani sabon tsari wanda ke magance matsalolin da ke tattare da hanyoyin koyon injin da ke da'ira a sama. Ta hanyar haɗa Blockchain, ɓoyayyen bayanai, da SDN, marubutan sun ƙirƙiri tsarin da zai ba da damar haɗin gwiwar ɓoyayyun bayanai tsakanin nodes maras aminci. Wannan yana da muhimmanci musamman a fannin kiwon lafiya, saboda bisa dokoki kamar HIPAA, sirrin bayanai yana da muhimmanci. Amfani da ɓoyayyen bayanai yana tabbatar da cewa bayanan suna ɓoyayye yayin lissafin, wannan fasahar kuma an jaddada ta a cikin aikin Gentry (2009) na cikakken ɓoyayyen bayanai. Idan aka kwatanta da koyon tarayya wanda har yanzu yana dogaro da uwar garken don tattarawa, wannan tsarin yana kawar da matsalolin da suka shafi madaidaicin batu, yana ƙarfafa tsaro da juriya. Duk da haka, kamar yadda binciken IEEE game da lissafin ɓoyayyen bayanai ya nuna, ƙarfin lissafin ɓoyayyen bayanai har yanzu kalubale ne. Haɗa SDN don inganta hanyar sadarwa wani mataki ne mai amfani, wanda ke rage jinkiri a cikin yanayin rarrabawa. Ta fuskar fasaha, tushensa na lissafi ya dogara ne da sifofin ɓoyayyen bayanai, misali, ga ƙari: idan $E(m_1)$ da $E(m_2)$ bayanai ne ɓoyayye, to $E(m_1 + m_2) = E(m_1) \oplus E(m_2)$. Wannan yana ba da damar tattarawa cikin aminci ba tare da buɗe kowane sabuntawa ba. Sakamakon simintin gyare-gyaren ya nuna kashi 92% na daidaito da rage kudin kaya, amma aiwatar da ainihin yana buƙatar magance matsalar fa'ida, saboda hanyoyin yarjejeniyar Blockchain kamar Proof of Work na iya zama jinkirin. An yi wahayi zuwa ga yanayin rarrabawar AI (kamar yadda aka tattauna a cikin binciken OpenAI game da koyon tarayya), wannan aikin ya yi daidai da canji zuwa lissafin gefe. Sabuntawa na gaba na iya bincika haɗa wannan tsarin tare da ƙirar gaurayawa tare da ɓoyayyen bayanai masu sauƙi, ko amfani da ci gaban ɓoyayyen bayanai na bayan quantum don magance barazanar quantum. Gabaɗaya, wannan tsarin yana wakiltar muhimmin mataki na demokratizaton AI yayin kiyaye sirri, ko da yake ainihin amfani zai dogara da daidaita tsaro da aiki.

8. References

1. Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security.
2. McMahan, B., et al. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics.
3. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. In STOC.
4. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
5. Yang, Q., et al. (2019). Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning.
6. Zyskind, G., et al. (2015). Decentralizing privacy: Using blockchain to protect personal data. In Security and Privacy Workshops.

Ƙarshe

Tsarin blockchain a matsayin sabis na al'ada ya ba da madadin amintacce, maras tsakiya ga na'urorin kwaikwayo na tushen girgije. Ta hanyar amfani da blockchain don kafa amana, ɓoyayyen bayanai don kare sirri, da SDN don haɓaka inganci, yana ba da damar haɗin gwiwa tsakanin rukunin yanar gizo, tare da kiyaye amincin bayanai. Aikin gaba zai mai da hankali kan haɓaka fa'ida da haɗa fasahar ɓoyayyar bayanai.