Is there any way to use an IP address to locate other places it was used?
Like using the IP address to see anything public that it did on the internet, from comments to a blog, to accessing a site?

IPs are more or less public, so I wondered if there is any way to know what else that is public was accessed by that IP.

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    Who do you mean by "you". Internet Browser user, Internet Provider, Three leter agencies? – humanityANDpeace Jan 10 '17 at 12:50
  • You, the regular guy, internet browserer. I know agencies can do it even without programs, mainly because isp is required to comply, internet provider has a list of it, too. – Anartes Jan 10 '17 at 13:04
  • In this case the answer of Philipp is quite right, in that there very little chance for you to get any information. Consider that the IP is something those folks relaying intenet traffic package see, the internet browsereres normally are not forwardinf packages, but are at the end of communications, therefore they cannot have any logs of traffic that existed when the ip was used by somebody else. – humanityANDpeace Jan 10 '17 at 13:11
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    you sometimes get lucky googling IPs... – dandavis Jan 10 '17 at 14:14

Only if:

  • the ISP of the user cooperates with you and tells you what sites they visited
  • those services cooperate with you by sharing their access logs with you

Neither is likely to cooperate with you unless you are a law enforcement officer and you have a search warrant signed by a judge.


That would be a severe privacy violation and would require all websites to share this information: this IP did that on my website. Someone should then group all that information.

But an IP can be considered PII (personal identifiable information) and thus sharing this kind of info is not allowed.

It seems as if you are trying to do something suspicious. Why would you like to know what a certain IP did on the internet?

  • ? what means PII – humanityANDpeace Jan 10 '17 at 12:49
  • personal identifiable information – Silver Jan 10 '17 at 12:49
  • I know IPs are easily seen, not long ago someone loged in my accounts repeatedly, and whenever I see the last machine it gets me to the same IP. – Anartes Jan 10 '17 at 12:57
  • I realised who he was by using it on the internet, so isp and approximate location, that made me paranoid of what else can be accessed so easy. – Anartes Jan 10 '17 at 12:58
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    Uh, IPs are not PII at all; quite the opposite. Most countries have legal precedent that explicitly separate the concept of identity and an IP address. They are considered privacy-sensitive information, but not personally identifying. – Polynomial Jan 10 '17 at 13:02

As addition to the above mentioned answers and comments.

If it's an abuse, how about an abuse-report to the ISP.

I'm not sure if the below document is what you are looking for. But I doubt someone can freely use such tools. I hope it helps.

A kind of Data mining could also be helpful, there are some documents online, perhaps a bit old.

3.6 Conclusions In this chapter we have presented a comprehensive discussion the Web personalization process viewed as an application of data mining which must therefore be supported during the various phases of a typical data mining cycle. We have discussed a host of activities and techniques used at different stages of this cycle, including the prepro- cessing and integration of data from multiple sources, and pattern discovery techniques that are applied to this data. We have also presented a number of specific recommen- dation algorithms for combining the discovered knowledge with the current status of a user’s activity in a Web site to provide personalized content to a user. The approaches we have detailed show how pattern discovery techniques such as clustering, association rule mining, and sequential pattern discovery, and probabilistic models performed on Web usage collaborative data, can be leveraged effectively as an integrated part of a Web personalization system. While a research into personalization has led to a number of effective algorithms and commercial success stories, a number of challenges and open questions still remain. A key part of the personalization process is the generation of user models. The most commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Even where more multi-dimensional or ontological information has been available, the data is generally mapped onto a single user-item table which is more amenable for most data mining and machine learning techniques. To provide the most useful and effective recommendations, personalization systems need to incorporate more expressive models. Some of the discussion on the integration of semantic knowledge and ontologies in the mining process suggests that some strides have been made in this direction. However, most of this work has not, as of yet, resulted in true and tested approaches that can become the basis of the next generation personalization systems. Another important and difficult of challenge is the modeling of user context. In par- ticular profiles commonly used today lack in their ability to model user context and dynamics. Users access different items for different reasons and under different con- texts. The modeling of context and its use within recommendation generation needs to be explored further. Also, user interests and needs change with time. Identifying these changes and adapting to them is a key goal of personalization. However, very little re- search effort has been expended the evolution of user patterns over time and their impact on recommendations. This is in part due to the trade-offs between expressiveness of the profiles and scalability with respect to the number of active users. Solutions to these important challenges are likely to lead to the creation of the next- generation of more effective and useful Web personalization and recommender systems that can be deployed in increasingly more complex Web-based environments. References

from here ore search google for "data mining for ip user profile" or perhaps "web usage mining".

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