Setting Up Private Analytics for Decentralized Video Platforms

Decentralized video platforms like Livepeer and Theta Network generate vast amounts of viewership data, but traditional analytics compromise user privacy. Private analytics use cryptographic techniques to derive aggregate insights—such as total watch time, unique viewers, and geographic distribution—without exposing individual user data. This is achieved by processing raw data through a zero-knowledge proof (ZKP) circuit, which outputs verifiable statistics. The core challenge is balancing data utility with privacy, ensuring creators and platforms can measure performance while adhering to web3's ethos of user sovereignty.

The technical stack for private analytics typically involves three components: a data collection layer, a privacy computation layer, and a verification/ storage layer. The data layer captures anonymized events (e.g., video play, pause, completion) using privacy-focused SDKs. These events are then batched and sent to a zk-SNARK or zk-STARK circuit, like those built with Circom or Halo2. The circuit performs computations on the encrypted data, generating a proof that the published metrics (e.g., '10,000 total views') are correct without revealing the underlying dataset. This proof and the resulting aggregate data are often published on-chain for transparency.

Here is a simplified conceptual example of a Circom circuit that privately counts video views. The circuit takes an array of hashed user IDs and view signals as private inputs, and outputs a public sum.

circomCopy
pragma circom 2.0.0;
template PrivateViewCounter(n) {
    signal input viewerHash[n]; // Private: hashed user identifier
    signal input viewed[n];     // Private: 1 if viewed, 0 otherwise
    signal output totalViews;   // Public: sum of views

    component adder = Sigma();
    adder.in <== viewed;
    totalViews <== adder.out;
}

This circuit ensures the totalViews output is verifiably correct, but the viewerHash and individual viewed signals remain hidden from the verifier.

For practical implementation, platforms must decide on an attestation model. One approach uses a decentralized network of oracles or attesters (like Pyth or Chainlink Functions) to collect signed data attestations from user clients. These signed messages become the private inputs to the ZK circuit. Alternatively, fully homomorphic encryption (FHE) schemes, as explored by Fhenix and Zama, allow computation on encrypted data before any decryption, offering another path for private aggregation. The choice depends on the trade-off between computational cost, proof generation time, and the required complexity of analytics.

Deploying this system requires careful integration with the video platform's infrastructure. The analytics pipeline should run off-chain or on a dedicated appchain (like a Rollup) to manage cost and latency, with only the final proof and key metrics settled on a base layer like Ethereum or Arbitrum. Developers should use libraries such as zkKit or SnarkJS for proof generation and verification. The end result is a dashboard where creators see verified analytics—like audience retention graphs or peak concurrent viewers—backed by cryptographic proofs, fostering trust without surveillance.

The core prerequisite is a Web3 development stack. You'll need Node.js (v18+), a package manager like npm or yarn, and a code editor such as VS Code. Essential libraries include ethers.js or viem for blockchain interaction, and ipfs-http-client or helia for decentralized storage. For the analytics backend, a framework like Express.js or Fastify is recommended. You should also have a basic understanding of smart contracts, as you'll interact with platform registries, content NFTs, and payment channels.

The system architecture follows a modular, off-chain design to preserve user privacy and scalability. The primary components are: a client-side SDK embedded in the video player, a privacy-preserving backend (your analytics server), and decentralized storage for processed data. The SDK collects anonymized events (play, pause, watch time) and sends them to your backend via encrypted channels. Crucially, the backend processes and aggregates this data before committing any hashed summaries to a public blockchain like Ethereum or a data-availability layer like Celestia, separating raw data from on-chain verification.

Data flow is critical. When a user watches a video, the SDK generates an event payload containing a session ID, content ID, and event type—never a wallet address or IP. This payload is encrypted and sent to your analytics backend via a secure API. The backend decrypts the data, processes it in-memory, and updates aggregate counters (e.g., total views per video). Periodically, a cryptographic commitment (like a Merkle root) of these aggregates is published on-chain. This allows the platform to verify analytics integrity without exposing individual user data, a pattern used by projects like Livepeer for verifiable metrics.

For storage, you have two layers. Processed aggregate data (daily views, engagement heatmaps) can be stored on IPFS or Arweave for permanence, with the Content Identifier (CID) recorded on-chain. Raw event logs should be handled ephemerally; consider temporary encrypted storage with automatic deletion after processing to minimize liability. Database choice is flexible: PostgreSQL or TimescaleDB are good for time-series aggregates, while Redis is ideal for real-time counters. Ensure all databases are configured with encryption at rest.

Finally, consider the deployment environment. You can host the backend on a traditional cloud provider (AWS, GCP) with robust security groups, or explore decentralized cloud options like Fleek or Akash Network for alignment with Web3 principles. Implement rate limiting, API key authentication for platform publishers, and use a service like Tenderly or Alchemy for reliable blockchain RPC access. This architecture ensures you can provide verifiable, trust-minimized analytics while maintaining the privacy expectations of a decentralized user base.

The first step is to install the @chainscore/analytics SDK into your frontend application. For a Node.js project, use npm: npm install @chainscore/analytics. For a direct browser implementation, you can load the SDK via a script tag from a CDN. The SDK is designed to be lightweight, adding minimal overhead to your video player's performance. Initialization requires your project's unique API key, which you can generate from the Chainscore dashboard after creating an application.

After installation, initialize the SDK in your application's entry point. The configuration object is critical for defining data collection boundaries and privacy rules. You must specify your projectId, set the environment (e.g., 'development' or 'production'), and configure the privacyLevel. Setting privacyLevel to 'high' enables features like local aggregation and differential privacy, ensuring individual viewing sessions cannot be traced back to specific wallet addresses or IPs.

The core integration involves instrumenting your video player. The SDK provides methods like trackPlayEvent(videoId, timestamp) and trackPauseEvent(videoId, duration). You should hook these into your player's native event listeners. For example, when using a player like video.js, you would listen for the 'play' and 'pause' events and call the corresponding Chainscore methods. This captures essential metrics—play rate, average watch time, and drop-off points—without collecting personal identifiable information (PII).

For advanced analytics, you can define custom events relevant to your platform's features. Use trackCustomEvent(eventName, properties) to log actions like 'liked_video', 'shared_video', or 'completed_chapter'. The properties object can include contextual data such as contentId and chapterNumber. All custom event data is processed through the same privacy pipeline, with properties hashed or aggregated before leaving the client, depending on your configuration.

Finally, you must configure the data submission endpoint. By default, encrypted event batches are sent to Chainscore's secure ingestion nodes. For enhanced decentralization, you can point the SDK to your own deployed node using the endpoint config option. Implement error handling to retry failed submissions and use the SDK's debug mode during development to log payloads to the console, verifying data structure and privacy filters are working correctly before going live.

The Event Ingestion API is the foundational component of your private analytics pipeline. It acts as a dedicated endpoint that your frontend application sends data to, replacing direct calls to centralized services like Google Analytics. Built using a framework like Node.js with Express or Python with FastAPI, its primary responsibilities are to authenticate requests, validate the incoming data schema, and securely enqueue events for asynchronous processing. This decouples the user experience from the analytics workload, ensuring video playback remains smooth.

A robust ingestion API must implement strict validation. Each incoming payload should be checked against a predefined schema using a library like zod or joi. Essential fields to validate include a unique sessionId, the videoId being interacted with, the eventType (e.g., play, pause, seek), and a timestamp. This prevents malformed or malicious data from corrupting your analytics database. The API should also verify a signature or API key to ensure events are only accepted from your legitimate application client.

After validation, events should be immediately placed into a durable message queue such as Apache Kafka, Amazon SQS, or Redis Streams. This is a critical design pattern for scalability and reliability. The API's job is to acknowledge receipt and respond quickly to the client; the queue ensures no events are lost if the downstream processing service is temporarily slow or unavailable. This architecture allows you to handle traffic spikes during a viral video launch without dropping data.

For a concrete example, here is a simplified Node.js/Express route for ingesting a play event:

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app.post('/api/ingest', async (req, res) => {
  const schema = z.object({
    sessionId: z.string(),
    videoId: z.string(),
    eventType: z.enum(['play', 'pause', 'seek', 'complete']),
    timestamp: z.number(),
    payload: z.object({ currentTime: z.number() }).optional()
  });
  const validation = schema.safeParse(req.body);
  if (!validation.success) {
    return res.status(400).json({ error: validation.error });
  }
  await kafkaProducer.send({
    topic: 'video-events',
    messages: [{ value: JSON.stringify(validation.data) }]
  });
  res.status(202).json({ status: 'accepted' });
});

Finally, consider implementing idempotency keys for critical events like video completions, where duplicate counting would skew metrics. The API can check a short-lived cache (e.g., Redis) for a processed key based on sessionId + videoId + eventType to prevent double ingestion. Once your events are flowing reliably into the message queue, the next step is to build the stream processor that consumes them, transforms the data, and loads it into your analytics datastore.

After collecting user interaction events, the next challenge is storing this sensitive data securely. While IPFS provides a robust, decentralized storage layer, its public nature means any file's content identifier (CID) can retrieve the raw data. For analytics on a video platform—which may include watch history, pause events, or content preferences—this public accessibility is a significant privacy violation. The solution is to encrypt the event data payload before pinning it to IPFS, turning the public network into a private, permissioned data store where only authorized parties with the decryption key can access the information.

Implementing this requires a client-side encryption step. Using a library like libsodium.js or the Web Crypto API, your application can encrypt the event batch before sending it to your pinning service. A common pattern is to generate a symmetric encryption key (e.g., using AES-GCM) derived from a user's wallet or a dedicated key management system. The encrypted data is then converted into a file and pinned to IPFS, returning a CID. This CID points to ciphertext, not plaintext, making the stored data useless to anyone who doesn't possess the key. The decryption key itself must be managed securely, often stored off-chain in the user's client or within a secure enclave.

The architecture now involves two critical pieces of metadata: the IPFS CID for the encrypted data and the decryption key. Typically, only the CID is stored on-chain or in a smart contract to maintain an immutable, verifiable record of the data's existence and location. For example, a user's analytics profile contract might store an array of CIDs, each representing a weekly batch of encrypted viewing events. The corresponding keys are managed separately, potentially using a service like Lit Protocol for decentralized key management or encrypted within the user's own wallet storage. This separation ensures the public ledger does not expose private data.

When the platform or the user needs to analyze this data, they must first retrieve the ciphertext from IPFS using the CID and then decrypt it using the private key. This process enables private computation over the data. For instance, to generate a personalized recommendation, a backend service authorized by the user could fetch several CIDs, decrypt the event histories, and run algorithms locally without ever exposing the raw data on a public server. This model aligns with privacy-by-design principles, giving users control over their data while enabling platform functionality.

Developers should consider a few key practices. First, encrypt-then-MAC (like AES-GCM) ensures both confidentiality and integrity. Second, key rotation strategies are important for long-term data; consider encrypting data with a unique key per batch, which is itself encrypted by a master key. Third, be mindful of gas costs: storing CIDs on-chain is cheap, but complex key management logic can be expensive. Frameworks like Ceramic Network or Tableland can offer alternative structured data layers for managing this metadata. Finally, always use reputable pinning services (like Pinata, nft.storage, or your own IPFS node) with appropriate authentication to ensure data availability.

By implementing private storage on IPFS, decentralized video platforms can leverage the permanence and resilience of decentralized storage without compromising user privacy. This step is foundational for building trust and compliance with data protection regulations. The next step involves querying and analyzing this encrypted data to derive insights, which requires setting up a secure computation environment or using zero-knowledge proofs for aggregate analytics without decryption.

After collecting the necessary witness data (e.g., user ID hash, video ID, timestamp, watch duration), the client-side application must generate a zero-knowledge proof. This proof is the core cryptographic object that allows the verifier to be convinced a statement is true without learning the underlying private inputs. For a view verification circuit, the proven statement is: "I possess a valid, non-revoked identity credential, and I watched a specific video for a duration exceeding the minimum threshold, without revealing which specific credential I used or my exact watch time." This process uses a ZK-SNARK proving system like Groth16 or PLONK.

The generation happens locally on the user's device using a proving key. This key is a public parameter specific to the circuit logic, often fetched from a decentralized storage service like IPFS. Using libraries such as snarkjs (for JavaScript/TypeScript) or arkworks (for Rust), the application runs the proving algorithm. It takes the private witness data and the public inputs (like the video ID and minimum watch time) as arguments. The output is a compact proof, typically just a few hundred bytes. This local computation ensures the user's raw data never leaves their device.

Here is a simplified conceptual example using pseudocode to illustrate the proving call:

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// Pseudocode using a snarkjs-like interface
const { proof, publicSignals } = await snarkjs.groth16.fullProve(
  {
    privateViewerIdHash: "0xabc...", // Private
    videoIdHash: "0xdef...",         // Public
    watchDuration: 750,              // Private (seconds)
    minDuration: 600                 // Public (threshold)
  },
  "./circuit_wasm/circuit.wasm",    // Compiled circuit
  "./proving_key/circuit_final.zkey" // Proving key
);

The publicSignals array contains the public outputs of the computation, which will be published on-chain alongside the proof.

The computational cost (proving time) is a critical consideration. For a simple view verification circuit, proving might take 2-5 seconds in a browser using WebAssembly. Complex circuits with many constraints will be slower. Optimizations include using trusted setups for smaller proofs (Groth16) or universal setups for easier updatability (PLONK). The proof and public signals are then ready to be submitted to the verifier smart contract on-chain, completing the user's action. This step transforms private activity into a publicly verifiable, privacy-preserving claim.

With your data pipeline securely collecting and storing user events, the next step is to generate aggregate analytics reports. This process involves querying your data warehouse (e.g., ClickHouse, PostgreSQL) to calculate key performance indicators (KPIs) that provide a holistic view of platform health and user engagement. Instead of analyzing individual user streams, you'll create summarized views that answer critical business questions, such as daily active viewers, average watch time per session, and content popularity trends. These aggregated datasets are the foundation for all executive dashboards and automated reporting.

To build these reports, you will write SQL queries or use a data transformation tool like dbt (data build tool). For example, to calculate daily metrics, you might create a daily_platform_metrics table that aggregates events from your raw_video_plays table. A sample query could be:

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SELECT
  DATE_TRUNC('day', timestamp) AS day,
  COUNT(DISTINCT user_id) AS daily_active_users,
  COUNT(*) AS total_plays,
  AVG(duration_seconds) AS avg_watch_time
FROM analytics.raw_video_plays
WHERE timestamp >= NOW() - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1 DESC;

This query rolls up billions of individual play events into a manageable time-series dataset of 30 rows, one for each day.

For decentralized platforms, consider aggregating metrics per content creator and per decentralized storage provider (e.g., IPFS, Arweave, Filecoin). This allows you to generate creator dashboards showing their audience demographics and performance, while also monitoring the reliability and latency of the underlying storage layer. You can join your event data with on-chain metadata from a service like The Graph to enrich reports with token-gated access stats or NFT ownership trends for specific video collections.

Finally, schedule these aggregation jobs to run automatically using a workflow orchestrator like Apache Airflow or Prefect. This ensures your reports are always up-to-date. The output—clean, aggregated tables—feeds directly into business intelligence tools (e.g., Metabase, Superset) for visualization. At this stage, you have moved from raw, private data to actionable, aggregate insights that can guide platform development, content strategy, and infrastructure investments without compromising individual user privacy.

Decentralized video platforms like Livepeer and Theta Network generate vast amounts of operational data, including transcoding job metrics, viewer engagement, and bandwidth usage. By integrating Chainscore, you can collect and analyze this data privately. For Livepeer, you can monitor orchestrator performance, job success rates, and earnings per round. For Theta, you can track edge node caching efficiency, video playback metrics, and TFUEL consumption. This data is crucial for optimizing network performance and user experience without exposing sensitive operational details.

To begin, you need to set up listeners for on-chain events from each protocol. For Livepeer, listen for TranscoderActivated, JobCreated, and Reward events on the Livepeer protocol contracts. For Theta, monitor the Theta blockchain for DepositStake, WithdrawStake, and GuardianVote events related to its Guardian Node and Edge Node operations. Use a service like The Graph to index this data efficiently or run your own indexer. Store the raw event data in a secure database, ensuring it's encrypted at rest and accessible only through your Chainscore API.

Next, process the raw event data into meaningful analytics. For Livepeer transcoding jobs, calculate metrics like average job duration, cost per pixel, and orchestrator uptime. For Theta edge caching, analyze cache hit ratios, data transfer volumes, and geographic distribution of viewers. Use a privacy-preserving computation framework, such as applying differential privacy or secure multi-party computation (MPC) techniques, before aggregating this data. This step ensures that individual node or user data cannot be reverse-engineered from the published analytics, maintaining network participant privacy.

Finally, expose the processed analytics through the Chainscore API. Create dedicated endpoints for each platform, such as GET /api/v1/livepeer/orchestrator/:id/metrics and GET /api/v1/theta/edge-cache/performance. Each endpoint should return aggregated, anonymized data in a standardized format (e.g., JSON). Implement API key authentication and rate limiting to control access. Developers building on Livepeer or Theta can then query these endpoints to build dashboards, automate payments based on performance, or trigger alerts for network issues, all while relying on Chainscore's private data layer.

The architecture you've implemented provides a robust foundation for understanding user engagement without compromising privacy. By leveraging decentralized storage like IPFS or Arweave for raw data, zero-knowledge proofs (ZKPs) for aggregation, and smart contracts on platforms like Ethereum or Polygon for access control, you create a system where insights are derived from verifiable, privacy-preserving computations. This stands in contrast to traditional analytics that rely on centralized data warehousing and user tracking.

For production deployment, consider these next steps. First, stress-test your data ingestion pipeline using tools like Tenderly or Hardhat to simulate high transaction volumes and ensure your event listeners and The Graph subgraphs remain performant. Second, implement a multi-sig wallet (using Safe or a custom Gnosis Safe module) to govern the analytics smart contract, adding a critical layer of security for managing query permissions and ZK verifier upgrades. Finally, explore privacy-preserving machine learning frameworks like zkML (e.g., using EZKL) to train recommendation models on your encrypted dataset, enabling features like personalized content feeds without exposing individual watch histories.

The field of private analytics is rapidly evolving. To stay current, monitor developments in fully homomorphic encryption (FHE) projects like Fhenix and Inco Network, which promise to enable computations on encrypted data without the need for ZK proofs. Engage with the research from organizations like the Ethereum Foundation's Privacy & Scaling Explorations team and 0xPARC. By building on the principles outlined here—decentralization, verifiability, and user sovereignty—you are contributing to a more sustainable and ethical foundation for Web3 media platforms.