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Collusion-Resistant Anonymous Data Collection Method Mafruz Zaman Ashrafi See-Kiong Ng Institute for Infocomm Research Singapore Introduction Quality data is a pre-requisite to obtain good data mining results. Collecting good quality data requires efforts and money. Internet is a convenient and low-cost platform for large-scale data collection. Some Motivating Examples Corporate Survey A large organization wishes to poll its employees for sensitive information. eg. How satisfied they are with their bosses’ management skills. - Individuals need to rate their bosses. - However, they are afraid of the price to pay for honesty. Health Information A drug company wishes to find out adverse effects of a drug. eg. Relationship between the effects of a drug with other drugs. - Patients need to disclose all the drugs they are taking. - However, disclosing drug info may reveal health condition. Traffic Monitoring Individual drivers wish to avoid roads with problematic conditions. eg. Find out the congested road intersections and other bottlenecks. - Individuals need to disclose their GPS info. - However, disclosing GPS info may reveal current position. Introduction Cont’d.. However, collecting data online has its challenges. Privacy is the number-one concern for online respondents. Respondents are reluctant to provide truthful information if their privacy is not protected. Technical Challenges Objective: Online Data Collection Two Actors: Data Collector and Respondents - The data collector wants to obtain the responses from a set of respondents. - The respondents submit honest responses only if the data collector is unable to link a particular response and its respondent. Challenges 1. How does the data collector guarantee that it is unable to associate a particular response to the corresponding respondent? 2. How can a collusion attack be mitigated? 3. How can an honest respondent pull out his response without revealing it to the data collector if he finds a threat to his anonymity? 4. How can we reduce the computational and communication overhead? Related Works 1. Randomized Response - Respondents’ responses are associated with the result of the toss of a coin. - Only a respondent knows whether the answer reflects the toss of the coin or his true experience. Pros: - A well-known technique. - Easy to use. Cons: - Adds noise to the result in response set that could distort the accuracy of the data mining results. Related Works Cont’d… 2. Cryptographic Techniques - Respondents employ two sets of keys to encrypt their responses before sending to the data collector. - Each respondent strips off a layer off encryption sequentially and shuffles decrypted results. - All respondents verify the intermediate results before the data collector obtains the actual response set. Pros: - A deterministic technique. - The data mining results are accurate. Cons: - Vulnerable against collusion attacks. - Higher communication overhead. Building Blocks of Our Approach 1. ElGamal Crypto - is a asymmetric public key encryption scheme. - is a probabilistic encryption. - achieves semantic security. - is malleable. 2. Substitution Cipher - Replace a character with another character. - Example: The Hybrid Model - Employs both ElGamal and Substitution Cipher. - Builds an Onion for a response. - Removes encryption layer (De-Onion) will result in the original response. An Onion Original response ElGamal Encryption Substitution Cipher ElGamal Encryption An Onion Layer The Hybrid Model Cont’d.. An example Onion De-Onion Original response 1234567890 9809364789 7893456720 2901560011 1234567890 9809364789 7893456720 2901560011 Original response The Protocol The Protocol The Protocol has five phases 1. Data Preparation 2. Data Submission 3. Anonymization 4. Verification 5. Decryption Phase I: Data Preparation Suppose there are 3 respondents (Alice, Bob and Carol). Bob’s Data Preparation Process Bob’s Original Response 1234 6652 5436 7065 1039 2309 3905 DM’s. Pri key 8902 Bob’s Sec. key 2453 Alice’s Sec. key 8091 Carol’s Sec. key 7609 9081 2098 8893 Bob’s Encrypted Response dBob Phase I: Data Preparation (cont’d..) Bob also computes an partial intermediate verification code WBob Bob Bob Alice Carol … … Alice … Carol … … WBob = 6652 4240 7056 bb … Phase II: Data Submission - Each participant submits an encrypted response i.e. and W to the data miner. The Data Miner - Computes the verification code ΩC = WBobWAlice WCarol - Encrypts ΩC using its secondary key and sends the result in encrypted value to each participant. - Shuffles response set {d1 , d2 , d3 } = { - Sends {d1 , d2 , d3 } to Carol. , , } Phase III: Anonymization - Carol “de-onions” one layer from each of the responses {d1 , d2 , d3 } . eg, Intermediate verification 8893 ElGamal Decryption 3905 Substitution De-Cipher 7056 ElGamal Decryption 5607 d’x Phase III: Anonymization (cont’d..) - … and computes intermediate verification Vcarol. Bob Alice Carol Carol Alice Bob VCarol = 7809 2291 …. …. …. …. …. …. - Shuffles the results in set {d’y ,d’z ,d’x} = { - Sends {d’y ,d’z ,d’x} to the Data Miner. 6790 VC , , } Phase III: Anonymization (cont’d..) - The Data Miner sends the randomize set {d’y ,d’z ,d’x} to next participant (eg, Alice) - Similar to Carol, Alice also ‘de-onion’ one layer from each element of {d’y ,d’z ,d’x}. - Computes intermediate verification. - Shuffles the results in set {d’p ,d’q ,d’r}={ - Sends {d’p ,d’q ,d’r} to the Data Miner. , , } Phase III: Anonymization (cont’d..) - The data miner sends {d’p ,d’q ,d’r} to the last participant (i.e. Bob), who ‘de-onion’ another layer from this set. - Computes intermediate verification, shuffles the result in set ‘S’= {d’m ,d’n ,d’o} and sends S to data miner. Phase IV: Verification - Data miner computes the final secondary encryption value ‘R’ from S. - Sends ‘R’ along with its secondary secret key to all participants. - Bob, Alice and Carol decrypt intermediate verification code they received at Phase 2. - They also compute ΩV and check ΩV = ΩC - If ok, each of them sends their secondary secret key to the data miner. Phase V: Decryption - Data miner uses the respondents’ secondary keys to strip off remaining encryption layers from S. - It uses its own primary key to strip off the final layer to reveal the original responses {….,1234,…..}. Results and Analysis Performance Analysis - Communication Overhead • Brickell et al. KDD 2006 Complexity - Computation - Respondent’s, O(N) - Data Miner, O(N2) - Communication - Participant’s, O(N) Conclusion The privacy of individual is an important issue in online data collection. Ignoring respondents’ privacy will result in inaccuracy in the data. Privacy-preserving online data collection must be (i) deterministic and (ii) efficient. Conclusion Deterministic: We employ crypto techniques Collusion Resistance: We incorporate onion/de-onion technique (using ElGama + Substitution) to create a protective layer against collusion Efficiency: Verification is done on single values instead of entire datasets Thank you Q&A The Protocol cont’d.. Suppose there are 3 respondents (Alice, Bob and Carol). 1. Data Preparation (Bob’s) Bob’s Original Response DM’s. Pri key 1234 8902 Bob’s Sec. key 2453 Alice’s Sec. key 8091 Carol’s Sec. key 7609 Bob’s Pri. key 2094 Alice’s Pri. key 5607 Substitution Cipher Carol’s Pri. key 4240 7056 Alice’s Pri. key Substitution Cipher 9081 3905 Bob’s Pri. key Carol’s Pri. key 1039 Substitution Cipher 8893 Bob’s Encrypted Response dBob - Bob generates a random number θ and computes ba = gθ and bb = gθ+7609 - Bob also generates WBob = 665242407056bb 6652 The Protocol cont’d.. Suppose there are 3 respondents (Alice, Bob and Carol). 1. Data Preparation (Bob’s) Bob’s Original Response DM’s. Pri key 1234 8902 Bob’s Sec. key 2453 Alice’s Sec. key 8091 Carol’s Sec. key 7609 Bob’s Pri. key 2094 Alice’s Pri. key 5607 Substitution Cipher Carol’s Pri. key 4240 7056 Alice’s Pri. key Substitution Cipher 9081 3905 Bob’s Pri. key Carol’s Pri. key 1039 Substitution Cipher 8893 Bob’s Encrypted Response dBob - Bob generates a random number θ and computes ba = gθ and bb = gθ+7609 - Bob also generates WBob = 665242407056bb 6652 Related Works Cont’d… 3. Mixed Networks - Respondents send response to an intermediate hop. - Each hop strips off a layer of encryption, which allows them to obtain the next hop’s address and forward the result to it. - The process continues till the response reached to the data collector. Pros: - Require less communication overhead. Cons: - Probabilistic approach and only works well if all participants and honest. - Intermediate hops can collaborate to breach an honest respondent’s anonymity. The Hybrid Model Cont’d.. An example Onion 1234567890 ElGamal Encryption 9809364789 Substitution Cipher 7893456720 ElGamal Encryption 2901560011 Original response De-Onion 2901560011 ElGamal Decryption 7893456720 Substitution De-cipher 9809364789 ElGamal Decryption 1234567890 Original response