Below is a point‐by‐point “fact check” and explanation of the key elements in the diagram, based on how mobile advertising technology actually works. In short, the overall flow shown—phone → SSP → DSP → advertisers/data brokers—is a fair, simplified representation of real ad‐tech data flows, though there are some oversimplifications and placeholders.
1) Collection of Device/Personal Data

    Location, IP address, timestamp, user agent, IDFA (Identifier for Advertisers), battery level, phone number (hashed), etc.
        Accurate in principle: Many advertising SDKs do try to collect these data points (or at least a subset of them) for targeting and analytics.
        IDFA is indeed the iOS advertising identifier; on Android devices, this would be the GAID (Google Advertising ID).
        Battery level and memory usage can be used to optimize ad rendering or even do “fingerprinting,” although not all SDKs gather all these extra data points.
        The IP address in the diagram (11.11.111.11) is clearly a placeholder—it’s not a typical “real” IP in most logs, but it illustrates the concept.

2) User Permission (“Allow app to track you?”)

    With iOS 14.5 and later, apps must prompt users for permission to access the IDFA.
    The diagram’s “Sure why not” arrow implies users often tap “Allow” without fully realizing the implications.
    If a user taps “No,” many SDKs still collect limited data (e.g., IP, device model, etc.) but do not get the IDFA.
        So the idea that there can be “Yes” or “No” to tracking is correct, but the rest of the data flow (particularly IP) can still occur even if the user opts out.

3) SSP (Supply‐Side Platform) and DSP (Demand‐Side Platform)

    SSP examples: Unity Ads, IronSource, Adjust. These are indeed known platforms that let app publishers “supply” ad inventory.
    DSP examples: Moloco (here humorously shown as “Moloocoads”), Criteo, etc. These platforms represent advertisers who “demand” ad space.
    Accurate Relationship: Data from the SSP is passed to DSPs so they can decide how much to bid on showing you an ad.

4) “Fake SSPs / Data Collectors”

    The diagram suggests some entities in the ad chain exist primarily to siphon data rather than to serve legitimate ads.
    In reality, there can be “resellers,” “data partners,” or questionable ad networks that mostly harvest data.
    While “Fake SSP” is not a formal ad‐tech term, the idea that some intermediaries might gather and monetize user data is absolutely valid.

5) Advertisers and Data Brokers

    Advertisers (e.g., “bwin,” “advertiser1,” “advertiser2” in the diagram) receive some level of user data when they bid on or serve ads.
    Data brokers (e.g., “Redmob,” “AGR”) can aggregate data and match it against other databases, sometimes re‐identifying “anonymous” IDs.
    Yes, data‐broker activity is real: They often buy/sell anonymized or pseudo‐anonymous data to companies, governments, or other third parties.

6) IDFA ↔ PII (Personally Identifiable Information)

    The diagram shows an example bridging the IDFA with PII (e.g., “Tim Smith,” phone number, address).
    Legally or policy‐wise, platforms usually claim they do not directly tie IDs (like IDFA) to names/addresses—but in practice, big data sets can facilitate re‐identification.
    So the diagram’s suggestion that advertisers/data brokers may link device IDs to real identities is (unfortunately) a known possibility.

7) Intelligence & Security Agencies

    The final arrow to “Intelligence & security agencies or anyone else…” is highlighting that in many jurisdictions, law enforcement or intelligence agencies can purchase location data from data brokers, or subpoena it.
    This is a recognized privacy concern and has been documented in various reports.

Bottom Line

    Overall Accuracy:
        The flow of data (phone → SSP → DSP → advertisers/data brokers → potential reidentification) is broadly correct in depicting how ad‐tech works behind the scenes.
        The specifics in the diagram (e.g., “Fake SSP,” example IP addresses) are stylized or simplified, but do represent real phenomena (questionable ad intermediaries, placeholders for IP addresses, and so on).

    Key Caveats:
        Consent frameworks (e.g., Apple’s ATT prompt, GDPR in Europe, CCPA in California) can limit or modify data sharing.
        The diagram shows an “idealized” or “worst‐case” data flow—actual implementations can vary, and some data might be hashed/anonymized.
        There are legal and technical constraints intended to prevent direct linking of IDFAs to raw PII, though these can be circumvented or worked around with data matching.

In short, yes, the image correctly illustrates the general “life cycle” of ad‐tech data and how numerous intermediaries (SSPs, DSPs, data brokers) can end up handling your information—even if some details are simplified for clarity.